What Comprises Clinical Experience in Recognizing Depression?: The Primary Care Clinician's Perspective
Bibliographic record
Abstract
PURPOSE: Depression is a highly prevalent condition in primary care settings. In our previously reported work, we investigated the processes and conditions that influence primary care clinicians' recognition of depression. Three conditions influence the recognition of depression: familiarity with the patient, time available, and clinical experience. This article further describes the role of clinical experience in depression care. METHODS: The grounded theory method was used to guide data collection and analysis. In-depth, in-person interviews were conducted with a purposeful sample of 8 clinicians. All interviews were audiotaped and transcribed. RESULTS: We identified 3 areas that comprise clinical experience relevant to depression care: (1) knowing one's professional role, (2) knowing oneself, and (3) knowing one's patients. In knowing one's professional role, 4 subdimensions were identified: (1) becoming familiar with illness patterns and clinical skills, (2) learning what works in the real world, (3) understanding what being a doctor is about, and (4) thinking of the whole person. The analysis indicated that clinical experience results from professional and personal growth during interactions with patients. The outcome of this developmental process was the achievement of comfort with depression care, a critical mediating variable that influenced primary care clinicians' recognition of depression. CONCLUSIONS: The developmental process of attaining comfort in managing depression warrants further exploration. Developing interventions to speed this process offers another approach to enhancing care for the management of 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".