Response to the comments on “Depression and anxiety symptoms among Afghan war widows and their associated factors: A cross-sectional analytical study”
Bibliographic record
Abstract
Dear Editor, We read the letter from the authors in response to our article (recently published in the December 12, 2024, issue of the Indian Journal of Psychiatry)[1] with interest. The authors were concerned about the study’s methodology and external validity. First, we agree with the authors’ comment that selecting study participants from healthcare settings is likely to introduce bias. However, we could not recruit our sample from community settings due to resource constraints and restrictions on community-based studies imposed by the Afghan government.[2] We reiterated the scope of the study in the limitations and conclusion sections. Second, we disagree with the authors’ comment that using prevalence rates from the suggested articles in our sample size calculation would have yielded more valid estimates. These studies focused on anxiety and depression symptoms in the general population rather than war widows.[3,4] Additionally, we would like to bring the authors’ attention to the publication dates of the suggested articles and our study timeline, as we began data collection in early 2023, which makes this comment less relevant. Third, as stated in the article, interviewers administered the 9-item Patient Health Questionnaire (PHQ-9) and the 7-item Generalized Anxiety Disorder Scale (GAD-7). We chose to have interviewers administer the PHQ-9 and GAD-7 scales (rather than have participants self-rate the scale) because previous studies have raised concerns about participant literacy in low- and middle-income countries (LMICs).[5] Fourth, due to strict word limitations in a brief research article, we were unable to discuss methods in more detail. In response to the author’s comments, we categorized participants’ education into two main groups: literate (including those with primary education, religious education, and higher studies) and illiterate (those with no education). Similarly, we consolidated household income sources into two broad categories: regular (monthly salary and private employment) and irregular (all other income sources). Fifth, in the statistical analyses, all assumptions for binary logistic regression were tested and met. Finally, we agree with the authors that it is difficult to say how much of the depression and anxiety symptoms were due to the loss of a husband versus a product of living in a post-conflict region. If there had been a reference group who were not war widows, it would have provided even more substantial evidence that the source of depression and anxiety symptoms was largely due to the loss of a husband. However, this does not compromise the importance of the study. Still, it is worth considering that the omission of a comparison group is an additional limitation that could be addressed in future research. We hope the authors found our responses satisfactory. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".