Suffering in Silence: Reasons for Not Disclosing Depression in Primary Care
Bibliographic record
Abstract
PURPOSE: Depression symptoms are underreported by patients. We thus assessed individuals' reasons for not disclosing depression to their primary care physician. METHODS: We conducted a follow-up telephone survey of 1,054 adults who had participated in the California Behavioral Risk Factor Survey System. Respondents were asked about reasons for nondisclosure of depressive symptoms to their primary care physician, depression-related beliefs, and demographic characteristics. Descriptive and inferential statistical procedures were used to characterize perceived obstacles to disclosure. RESULTS: Of the respondents, 43% reported 1 or more reasons for nondisclosure. The most frequent reason was the concern that the physician would recommend antidepressants (22.9%; 95% confidence interval, 18.8%-27.5%). Reported reasons for nondisclosure of depression varied based on whether the respondent had a history of depression. For example, respondents with no depression history were more likely to believe that depression falls outside the purview of primary care (P=.040) and more likely to fret about being referred to a psychiatrist (P=.036). Respondents with clinically significant depressive symptoms rated 10 of 11 barriers to disclosure as more personally applicable than did those without symptoms (all P values =.014). Number of reported disclosure barriers was predicted by demographic characteristics (being female, Hispanic, of low socioeconomic status), depression beliefs (depression is stigmatizing and should be under one's control), symptom severity, and absence of a family history of depression. CONCLUSIONS: Many adults subscribe to beliefs likely to inhibit explicit requests for help from their primary care physician during a depressive episode. Interventions should be developed to encourage patients to disclose their depression symptoms and physicians to ask about depression.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".