1. Define an establishing operation and provide characteristics of an establishing operation.
2. Discuss the recent movement to the term motivation operation.
3. Describe the difference between motivating operations and discriminative relations.
4. Define unconditioned motivation operations (UMOs).
5. State various examples of UMOs as related to the human organism.
6. Define the three types conditioned motivating operations (CMOs).
7. Provide two examples of each type of CMO as related to the human organism.
8. Name some general implications for the use of MOs in the study of behavior analysis.

Homework 2, Machine Learning, Fall 2022
*IMPORTANT* Homework Submission Instructions
1. All homeworks must be submitted in one PDF file to Gradescope.
2. Please make sure to select the corresponding HW pages on Gradescope for each question.
3. For all theory problems, please type the answer and proof using Latex or Markdown, and export the
tex or markdown into a PDF file.
4. For all coding components, complete the solutions with a Jupyter notebook/Google Colab, and export
the notebook (including both code and outputs) into a PDF file. Concatenate the theory solutions
PDF file with the coding solutions PDF file into one PDF file which you will submit.
5. Failure to adhere to the above submission format may result in penalties.
All homework assignments must be your independent work product, no collaboration is allowed.
You may check your assignment with others or the internet after you have done it, but you must
actually do it by yourself. Please copy the following statements at the top of your assignment file:
Agreement 1) This assignment represents my own work. I did not work on this assignment with others.
All coding was done by myself.
Agreement 2) I understand that if I struggle with this assignment that I will reevaluate whether this is
the correct class for me to take. I understand that the homework only gets harder.
1
to
1 Convexity (Chudi)
Convexity is very important for optimization. In this question, we will explore the convexity of some
functions.
1.1
Let h(x) = f(x) + g(x) where f(x) and g(x) are both convex functions. Show that h(x) is also convex.
1.2
In mathematics, a concave function is the negative of a convex function. Let g1(x), g2(x), …., gk(x) be concave
functions and gi(x) ! 0. Show that log(
Qk
i=1 gi(x)) is concave. (Note the base of log function is e.)
Hint 1: the sum of two concave function is concave.
Hint 2: log(a ⇥ b) = log(a) + log(b).
Hint 3: log function is concave and monotonically increasing in R+.
1.3
A line, represented by y = ax + b, is both convex and concave. Draw four lines with di↵erent values of a
and b and highlight their maximum and minimum in two di↵erent colors. What does this reveal about the
convexity of the maximum and minimum of these lines respectively?
1.4
Now we consider the general case. Let f(x) and g(x) be two convex functions. Show that h(x) =
max(f(x), g(x)) is also convex or give a counterexample.
1.5
Let f(x) and g(x) be two convex functions. Show that h(x) = min(f(x), g(x)) is also convex or give a
counterexample.
1.6
Since convex optimization is much more desirable then non-convex optimization, many commonly used risk
functions in machine learning are convex. In the following questions, we will explore some of these empirical
risk functions.
Suppose we have a dataset {X, y} = {xi, yi}ni
=1 that we will perform classification on, where xi 2 Rp is a
p-dimensional feature vector, and yi 2 {−1, 1} is the binary label. Please identify and show by proof whether
the following empirical risk functions are convex with respect to weight parameter vector !. In the model
below, ! = [!1,!2, …,!p]T 2 Rp, b 2 R are the weight and bias. We would like to show convexity with
respect to ! and b.
(a)
L(!, b) =
Xn
i=1
log(1 + e−yi(!T xi+b))
(b)
L(!, b,C) =
1
2||!||22
+ C
Xn
i=1
max(0, 1 − yi(!Txi + b)), C! 0
Page 2
1.7
Consider the lasso regression problem. Given a dataset {X, y} = {xi, yi}ni
=1 where yi 2 R, please identify and
show by proof whether the following empirical risk function is convex with respect to the weight parameter
vector !.
L(!, b,C) = ky − (X! + b1n)k22
+ Ck!k1, C! 0
2 Gradient Descent and Newton’s Method (Chudi)
Optimization algorithms are important in machine learning. In this problem, we will explore ideas behind
gradient descent and Newton’s method.
Suppose we want to minimize a convex function given by
f(x) = 2×21
+ 3×22
− 4×1 − 6×2 + 2x1x2 + 13.
2.1
(a) Find the expression for rf(x), the first derivative of f(x).
(b) Find the expression for Hf(x), the Hessian of f(x).
(c) Compute analytically the value of x⇤, at which the optimum is achieved for f(x). Hint: use the first
two parts.
2.2
Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a di↵erentiable
function. Since f(x) is strictly convex and di↵erentiable, gradient descent can be used to find the
unique optimal solution.
Recall that gradient descent takes repeated steps in the opposite direction of the gradient of the function
at the current point until convergence. The algorithm is shown below. Given an initial point x0 and a step
size ↵,
• t = 0
• while xt is not a minimum
– xt+1 = xt − ↵rf(xt)
– t = t + 1
• end
For each iteration, xt+1 = xt −↵rf(xt). Suppose ↵ = 0.1. Show that xt+1 converges to x⇤ obtained in
2.1 as t!1.
Hint 1: Let A be a square matrix. I + A + A2 + . ..Ak = (I − A)−1(I − Ak+1).
Hint 2: Let A be a square matrix. Ak converges to zero matrix if all eigenvalues of matrix A have absolute
value smaller than 1.
2.3
Will xt+1 converge to x⇤ when ↵ grows larger? Hint: the previous question’s calculations should provide
you the answer.
Page 3
2.4
Gradient descent only uses the first derivative to make updates. If a function is twice di↵erentiable, is the
second derivative able to help find optimal solutions? Newton’s method takes the second derivative into
consideration and the key update step is modified as xt+1 = xt − (Hf(xt))−1rf(xt). The algorithm is
shown below.
• t = 0
• while xt is not a minimum
– xt+1 = xt − (Hf(xt))−1rf(xt)
– t = t + 1
• end
(a) Using the initial point x0 = [1, 2]T , give the value of x1 that Newton’s method would find in one
iteration. Does this method converge in one iteration? If not, give the value of x2 that Newton’s
method would find and report whether x2 is equal to x⇤.
(b) Using the initial point x0 = [1, 2]T and a fixed step size ↵ = 0.1, give the value of x1 that gradient
descent would find in one iteration. Does this method converge in one iteration? If not, give the value
of x2 that gradient descent would find and report whether x2 is equal to x⇤.
(c) According to the results above, which method converges faster? Please briefly explain why it happens.
3 Bias and Variance of Random Forest
In this question, we will analyze the bias and variance of the random forest algorithm. Bias is the amount
that a model’s prediction di↵ers from the target value in the training data. An underfitted model can lead
to high bias. Variance indicates how much the estimate of the target function will alter if di↵erent training
data were used. An overfitted model can lead to high variance.
3.1
Let {X, y} = {(xi, yi)}ni
=1 be the training dataset, where xi is a feature vector and yi 2 {−1, 1} is the binary
label. Suppose all overlapping samples are consistently labeled, i.e. 8i 6= j, yi = yj if xi = xj . Show that
100% training accuracy tree can be achieved if there is no depth limit on the trees in the forest. Hint: the
proof is short. One way to proof it is by induction.
3.2
Let D1,D2, …,DT be random variables that are identically distributed with positive pairwise correlation ⇢
and V ar(Di) = $2, 8i 2 {1, …,T}. Let ¯D be the average of D1, …,DT , i.e. ¯D = 1
T (
PT
i=1 Di). Show that
V ar(¯D ) =
1 − ⇢
T
$2 + ⇢$2. (1)
3.3
In the random forest algorithm, we can view the decision tree grown in each iteration as approximately Di
in Problem 3.2. Given this approximation, briefly explain how we expect the variance of the average of the
decision trees to change as T increases.
Page 4
3.4
As T becomes large, the first term in Eq(1) goes away. But the second term does not. What parameter(s)
in random forest control ⇢ in that second term?
4 Coding for SVM (Henry)
4.1
Generate a training set of size 100 (50 samples for each label) with 2D features (X) drawn at random as
follows:
• Xneg ⇠ N([-5, -5], 5·I2) and correspond to negative labels (-1)
• Xpos ⇠ N([5, 5], 5·I2) and correspond to positive labels (+1)
Accordingly, X = [Xpos
Xneg ] is a 100 ⇥ 2 array, Y is a 100 ⇥ 1 array of values 2 {−1, 1}. Draw a scatter plot of
the full training dataset with the points colored according to their labels.
4.2
Train a linear support vector machine on the data with C = 1 and draw the decision boundary line that
separates positives and negatives. Mark the support vectors separately (e.g., circle around the point).
4.3
Draw decision boundaries for 9 di↵erent C values across a wide range of values between 0 and infinity. Plot
the number of support vectors vs. C (plot on a log scale if that’s useful). How does the number of support
vectors change as C increases and why does it change like that?
4.4
Now, try to rescale the data to the [0,1] range and repeat the steps of the previous question (4.3) and over
the same range of C values. Are the decision boundaries di↵erent from those in the previous question? What
does this imply about (a) the geometric margin and (b) the relative e↵ect of each feature on the predictions
of the trained model?
4.5
Try using boosted decision trees (using any boosted decision tree algorithm you can find) with and without
changing the scaling of the dataset and plot both decision boundaries on one plot. Do the plots di↵er? Is
that what you expected? (Make sure you use the same random seed for both runs of your algorithm.)
5 Coding for Logistic Regression (Henry)
Download the Movie Review data file from Sakai named moviereview.tsv. The data file is in tab-separatedvalue
(.tsv) format. The data is from the Movie Review Polarity data set (for more details, see http:
//www.cs.cornell.edu/people/pabo/movie-review-data/). Currently, the original data was distributed
as a collection of separate files (one movie review per file). The data we are using converted this to have
one line per example, with the label 0 or 1 in the first column (the review is negative or positive) followed
by all of the words in the movie review (with none of the line breaks) in the second column. We provide
a dictionary file to limit the vocabulary to be considered in this assignment. The dictionary.txt uses the
format: [word, index], which represents the kth word (so it has the index k) in the dictionary.
Page 5
5.1
In this section, your goal is to derive the update formula for the logistic regression when optimizing with
gradient descent. Gradient descent is as follows:
To optimize J(✓, x, y)
choose learning rate ⌘
t as current time, T as max time
✓0 ! initial value
while t < T: do
Update: ✓j ! ✓j − ⌘ · @J(✓,x,y)
@✓j
end while
Assume you are given a data set with n training examples and p features. We first want to find the
negative conditional log-likelihood of the training data in terms of the design matrix X, the labels y, and the
parameter vector ✓. This will be your objective function J(✓) for gradient descent. Here we assume that each
feature vector xi,· contains a bias feature, that is, xi,1 = 1 8i 2 1, …,n. Thus, xi,· is a (p + 1)-dimensional
vector where xi,1 =1. As such, the bias parameter is folded into our parameter vector ✓.
(a) Derive the negative log-likelihood of p(y|X, ✓). (Hint: look at the course notes.)
(b) Then, derive the partial derivative of the negative log-likelihood J(✓) with respect to ✓j , j 2 1, …,p + 1.
(c) The update rule is ✓j ! ✓j − ⌘ · @J(✓)
@✓j
. Based on the update rule, write the gradient descent update
for parameter element ✓j .
We will implement this momentarily.
5.2
In this section, you are going to do some preprocessing. You are going to create something similar to a
bag-of-words feature representation, where the feature vector xi,v for movie review i and vocabulary word v
in the dictionary is set to 1 if movie review i contains at least one occurrence of vocabulary v. An example
of the output from your pre-processing for one movie review might be as follows: 20:1 22:1 30:1 35:1 45:1
……., which expresses that vocabulary words 20, 22, 30, 35, 45, etc. are in the movie review. The feature
vector ignores words not in the vocabulary of dict.txt. You can use csv and math packages in python.
5.3
In this section, implement a sentiment polarity analyzer using binary logistic regression. Specifically, you
will learn the parameters of a binary logistic regression model that predicts a sentiment polarity (i.e., the
label) for the corresponding feature vector of each movie review using gradient descent, as follows:
• Initialize all model parameters to 0.
• Use gradient descent to optimize the parameters for a binary logistic regression model. The number
of times gradient descent loops through all of the training data (number of epochs). Set your learning
rate as a constant ⌘=0.1.
• Perform gradient descent updates on the training data for 30 epochs.
Do not hard-code any aspects of the data sets into your code. You should only use the packages that are
provided. You may use sklearn for creating the train/test split. You can use csv and math packages in python.
Page 6
Let us provide a note about efficient computation of dot-products. In linear models like logistic regression,
the computation is often dominated by the dot-product ✓T x of the parameters ✓ 2 Rp with the feature vector
x 2 Rp. When a standard representation of x is used, the dot-product requires O(p) computation, because it
requires a sum over all entries in the vector: ✓T x =
Pp
j=1 ✓jx·,j However, if our feature vector is represented
sparsely, we can observe that the only elements of the feature vector that will contribute a non-zero value to
the sum are those where x·,j 6= 0, since this would allow ✓jx·,j to be nonzero. This requires only computation
proportional to the number of non-zero entries in x, which is generally very small for model compared to
the size of the vocabulary.
6 Boosted Decision Trees and Random Forest (Henry)
In this assignment, you are going to fit and tune random forest and boosted decision trees to predict whether
a given passenger survived the sinking of the Titanic based on the variables provided in the data set. You
may use sklearn and gridsearch.
6.1
First, download the Titanic.csv file from Sakai, and drop the columns that have “na” values from the data
set. Convert the gender variable into a binary variable with 0 representing male, and 1 representing female.
Then split 80% of the data into train and 20% of the data into test set.
6.2
Fit a random forest and boosted decision tree model on the training set with default parameters, and
estimate the time it required to fit each of the models. Which model required less time to train? Why do
you think that is? (Hint: look at the default values of the parameters.)
6.3
Choose a range of parameter values for each of the algorithms (tell us what parameters you played with),
and tune on the training set over 5 folds. Then, draw the ROC and provide the AUC for each of the
tuned models on the whole training set (in one figure) and test set (in another figure). Comment on the
similarities/di↵erences between the two models (if any).
7 Generalized Additive Models
In this question, you are going to fit a generalized additive model (GAM) to predict the species of penguins on
a given dataset. You can download the dataset penguins trunc.csv from Sakai. The dataset is preprocessed
and you can use it directly.
Split 80% of the data into a training set and 20% of the data into a test set. Please use one of the
following methods to fit a GAM model: (1) logistic regression with `1 penalty, (2) Explainable Boosting
Machine, (3) FastSparse, (4) pyGAM. Report training and test accuracy and plot the shape function for
each feature on a separate plot.
Hint 1: For logistic regression with `1 penalty, you can use sklearn packages. You need to first transform
each continuous feature into a set of binary features by splitting on the mid-point of every two consecutive
values realized in the dataset, i.e., CulmenLength  32.6, CulmenLength  33.3. You can then fit regularized
logistic regression to these indicator variables and construct the component function for variable j by
weighting the indicator variables that pertain to feature j by their learned coefficients and adding them up.
You can use sklearn LogisticRegression. Please remember to set penalty to “`1” and algorithm to “liblinear”.
Hint 2: Explainable Boosting Machine is a tree-based, cyclic gradient boosting generalized additive modeling
package. It can be pip installed for Python 3.6+ Linux, Mac, Windows. More details about the algorithm and
Page 7
usage are available in https://github.com/interpretml/interpret and https://interpret.ml/docs/
ebm.html.
Hint 3: FastSparse implements fast classification techniques for sparse generalized linear and additive models
in R. To install this package, please follow the instruction here https://github.com/jiachangliu/
fastSparse/tree/main/installation. Example code about how to run FastSparse is available https:
//github.com/jiachangliu/fastSparse/tree/main/application_and_usage and example code to visualize
shape functions is https://github.com/jiachangliu/fastSparse/tree/main/visualization.
Hint 4: You may also use pygam package to fit a GAM model which is available here https://github.com/
dswah/pyGAM.
Page

Assignment:

1) For this assignment, draft and post a reply that briefly lists and describes each of the project schedule artifacts (Schedule Management Plan/Statement, Project Milestone List, Resource Requirements, Project Roles and Responsibilities, and the Project Schedule)
2) Attach each of the artifacts to your initial reply for peer review.

Insomnia is one of the most common medical conditions you will encounter as a PMHNP. Insomnia is a common symptom of many mental illnesses, including anxiety, depression, schizophrenia, and ADHD (Abbott, 2016). Various studies have demonstrated the bidirectional relationship between insomnia and mental illness. In fact, about 50% of adults with insomnia have a mental health problem, while up to 90% of adults with depression experience sleep problems (Abbott, 2016). Due to the interconnected psychopathology, it is important that you, as the PMHNP, understand the importance of the effects some psychopharmacologic treatments may have on a patient’s mental health illness and their sleep patterns. Therefore, it is important that you understand and reflect on the evidence-based research in developing treatment plans to recommend proper sleep practices to your patients as well as recommend appropriate psychopharmacologic treatments for optimal health and well-being.

Reference: Abbott, J. (2016). What’s the link between insomnia and mental illness? Health. https://www.sciencealert.com/what-exactly-is-the-l…

For this Discussion, review the case Learning Resources and the case study excerpt presented. Reflect on the case study excerpt and consider the therapy approaches you might take to assess, diagnose, and treat the patient’s health needs.

Case: An elderly widow who just lost her spouse.

Subjective: A patient presents to your primary care office today with chief complaint of insomnia. Patient is 75 YO with PMH of DM, HTN, and MDD. Her husband of 41 years passed away 10 months ago. Since then, she states her depression has gotten worse as well as her sleep habits. The patient has no previous history of depression prior to her husband’s death. She is awake, alert, and oriented x3. Patient normally sees PCP once or twice a year. Patient denies any suicidal ideations. Patient arrived at the office today by private vehicle. Patient currently takes the following medications:

• Metformin 500mg BID
• Januvia 100mg daily
• Losartan 100mg daily
• HCTZ 25mg daily
• Sertraline 100mg daily

Current weight: 88 kg

Current height: 64 inches

User generated content is uploaded by users for the purp

For this assignment you will choose, and carefully read, two published classical music concert reviews from any of the sources listed below. One review should be related to a vocal performance and the other review should be related to an instrumental performance. The objective of this assignment is for you to learn how music reviewers who attend classical music concerts discuss the music and performances they experience in live performance settings. As you write your papers be sure to give the title and source of the reviews, the name of the reviewers, and the type of musical performances being reviewed. Include any other pertinent information such as the date, location (performance venue), name of the group or individuals performing, and works and composers on the program. Do not do a comparison of the two reviews! As you read each review take note of and discuss the different types of information given by the reviewers. Report on each reviewer’s writing style focusing on their use of vocabulary (academic or conversational), organization, tone (e.g. critical, informative, etc.), and potential target audience. Assess the overall effectiveness of the passage in conveying the information related to the works on the program, the performers, the performance space, and the performance itself. Be prepared to share some of your observations about the reviews in our class discussion and feel free to use short quotes from select passages of the reviews or your paper in your discussions.

Clearly, in a General Education course on “The World of Fiction,” we are not expecting to study ONLY “high literature” (like Shakespeare or, for that matter, Henry James). So, take the time and space here to reflect a bit on what you think about the relationship between genre fiction (like YA, Sci-Fi/Fantasy, Dystopian, Graphic Novels, or Detective Fiction) and the society and culture that produced it.

***Not that any of these genres need “defending,” but how MIGHT you respond if someone suggested that there was no value to reading and/or studying genre fiction? What can we possibly benefit from not only READING but actually STUDYING a field like detective fiction?

Please DO NOT PLAGARIZE. DO NOT COPY AND PASTE.

The response should be at least 150 Words before citations.

2. Respond to these two posts with 100 words each before citations;

Number 1: Books have a limitation on how they can convey messages while a play, movie or even a video game can incorporate sounds, visual elements, and even direct interaction, there are only so many ways you can write a sentence. If you need to change the tone or inlaid message of a piece, you can try and write around it which is honestly a difficult thing that not everyone can pull off 100% of the time without messing with the flow of the writing overall. A simpler way to get around this issue is formatting and structure changes. By moving away from a more uh traditional style of 3rd person narration you can drastically shift the way the words will feel and come across as. Reading off a block of exposition from a narrator can be boring and feel lazy on the author’s part. When formatted as a letter from that character to another the writing can be far more open and it also takes on a more personal note of the character writing it. It also can be used for pacing allowing the reader to pull themselves free of the little details that preceded the letterhead and let them think on the two parts separately.

Number 2: I think that, in many instances, letters do play a big role in communication. When we read Mary Shelley, we were also guided through Walton’s letters, and these letters served as a form of communication between the sender and the receiver, and they also provided us with important details. In The Turn of the Scew, we witness a similar situation as Douglas sent the first letter, although with a key in it which is what makes it a mystery in a way due to the fact that the key is undescribed for its purpose. In the beginning of the story, we are aware of how these letters are being brought up, and at some point, we even see the governess receiving a letter regarding Miles’s expulsion from school. Going back, someone wrote that letter which was the headmaster at Miles school wrote and send to governess and then someone reads it, and well in this case they cannot read which leads to the counselor as the governess shows her the letter. There is a connection between these letters that does lead to a big mystery in what they represent symbolically, it is hard to understand, but we can analyze that it is a form of communication to communicate a specific message, perhaps linking it to the key.

Fundamentals of Nursing Models, Theories, and Practice discusses the theory-practice gap in detail in many chapters. As you have read throughout the course, there is ongoing discussion about the connection between theory and practice and the application in day-to-day nursing activities. This reflection is designed to illustrate that although there may be a gap, other factors play an important role in decision-making and each aspect of theory, research, and practice experience are integral to well-rounded patient care.

Access Fundamentals of Nursing Models, Theories, and Practice and review Figure 1.4 Correlation: Education, Science and Practice on page 11.

Post 2 substantive replies to classmates. Be constructive and professional.

Repsonse 1#

Patient is 64-year-old, married female presenting in her PCP’s office because her husband has concerns of her coughing – the frequency and amount. Patient has been smoking for 25 years, diagnosed with asthma 10 years ago – patient does not feel it’s necessary to renew medication for asthma because she feels as if it is not working. When asked about her general health – patient states she feels overall healthy, her husband chimes in and appears to know about her health, providing more necessary details.

Regarding the scenario, I created above, I choose to apply Dorothea Orem’s Self-Care Deficit Theory. Orem’s theory is comprised of three related parts: theory of self-care; theory of self-care deficit; and theory of nursing system (Orem’s Self-Care Deficit, 2020). Self-care pertains to the activities a patient can perform on their own regarding maintaining life, health, and well-being. Self-care deficit is when nursing care is required because the patient is either incapable or limited in providing self-care. Lastly, the nursing system is the relationship formed between the nurse and the patient – the nursing system is activated when the self-care deficits force the nurse to intervene and provide care to the patient. Orem explained that well-being is used in the sense of an individual’s ‘perceived condition of existence’ (McKenna et al., 2014). One of the main strengths with Orem’s theory is that is can be applied to that of a novice nurse all the way to an expert nurse – it is comprehensive approach to nursing. In a primary care setting – Orem’s theory allows for the provider to communicate effectively to the patient while contributing to the patient’s overall health and well-being. There are limitations with Orem’s theory just like other theories especially when applying them to different cases or settings. The case provided above does not account for “family” –the patient’s husband. Orem’s theory focuses on the individual’s ability not accounting for the individual’s support system in which some cases including the above scenario – the patient’s husband better understands and better manages the patient’s care. Another limitation is that Orem’s theory does not account for a patient’s emotional needs or explain how to handle or account for the dynamic aspect of the patient’s health and how it can be every-changing (Gonzalo, 2021a).

References

Gonzalo, A. B. (2021a, March 5). Dorothea Orem: Self-Care Deficit Theory. Nurseslabs. https://nurseslabs.com/dorothea-orems-self-care-th…

Mckenna, H., Pajnkihar, M.,& Murphy, F. (2014). Fundamentals of nursing models, theories and practice (2nd ed.) Wiley.

Orem’s Self-Care Deficit. (2020, July 19). Nursing Theory. https://nursing-theory.org/theories-and-models/ore…

Response 2#

Kathryn E. Barnard developed the Child Health Assessment Model with the aim of improving the health of infants and their families (Nurselabs, 2021) Barnard’s theory focused on the importance of the way they were treated and cared for from birth onward into their childhood. The children were evaluated in several areas, stimulation of the senses, social, emotional, and behavioral growth (Weber, B. 2015). Barnard did extensive research on premature newborns who were isolated in incubators and lacked physical touch and interaction. She developed the Isolette which rocks the baby as they are in the incubator, and the result was improved weight gain and more motor and sensory function advancement (Weber, B., 2015). The development of her theory developed a standard in care of premature babies. The consistent positive outcomes for the infants showed her theory qualified as a contribution to nursing science (McKenna, H., Pajnkihar, M., & Murphy, F., 2014).

The gaps in Barnard’s theory could be the variables such as the socioeconomic status of the family, meaning resources available for food, parenting classes, community resources and support. Another gap regarding variables is the health of the child. Children with health problems such as cerebral palsy would not be able to be compared to a healthy child in the area of senses and behavioral growth. The medical diagnosis of ‘Failure to Thrive’ is another potential gap. “Because the causes of organic and environmental weight loss in the infant are numerous, failure to thrive (FTT) is a difficult diagnosis McPeters, S. L., Bryant, P. H., & Speck, P. M. (2021).”

With the gaps that are a possibility in Barnard’s theory it is easy to appreciate that each infant is individualized and must be evaluated on a case-by-case basis. There are factors that will determine the child’s development as they grow, and these could also affect the way they develop in addition to the nurturing and care they receive as a child.

Discussion Information

Take a moment to imagine the following:

A child exhibits a problematic behavior. A parent sees this and disciplines the child.

• What does the child look like?
• Who is the parent?
• What is the setting like?
• What type of discipline is used?

Did your image include corporal punishment? In some communities and groups in the United States and elsewhere, corporal punishment, such as spanking, is an appropriate form of discipline.

In this discussion, we’ll explore the discipline styles used by different groups of US parents, and then compare these findings to research about parental disciplines used in other cultures.

We’ll also discuss outcomes associated with these disciplinary styles.

Discussion Topics/Question

In your initial response to this forum, please address all of the following:

• Differentiate between physical abuse, physical neglect, and corporal punishment.
• Next, locate at least one scholarly source (such as a peer-reviewed journal article or document from a governmental agency’s or professional association’s website) about the way(s) discipline is approached in an international culture.
• As discussed in Berk (2018), corporal punishment is used more so by some cultural groups, domestically and abroad. Compare and contrast the information you found to that offered by Berk (2018).
• Does spanking contribute to future problem behavior in childhood, adolescence, and/or adulthood?
• How does your understanding of cognitive and/or socioemotional developmental theories inform your response?