KEEPING IT TOGETHER: HOW WOMEN USE THE BIOMEDICAL EXPLANATORY MODEL TO MANAGE THE STIGMA OF DEPRESSION
Bibliographic record
Abstract
Although considerable research has been conducted on women who are depressed, the actual experiences and voices of women have not been central to this research. Therefore little is known about how women make sense of depression as they live with and manage it in their daily lives. Our purposes in doing this study were to (1) examine how women experience and manage depression and treatment, and (2) investigate the core components of women's explanatory models of depression (including beliefs about etiology, onset of symptoms, pathophysiology, course of illness, and treatment needs). We interviewed 43 women living in a small city in Western Canada who had sought treatment within the previous five years. Data were analyzed using the constant comparison method of grounded theory. In this paper we will focus on the core concept, Keeping it Together, and its three supporting categories, (1) Taking Up a Biomedical Explanation for Depression, (2) Using the Biomedical Explanatory Model (BEM) to Manage the Stigma of Depression, and (3) The Inadvertent Effects of Adopting a BEM.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".