Decision‐making needs of patients with depression: a descriptive study
Bibliographic record
Abstract
The study's purpose was to explore the decision-making needs of patients considering treatment options for their depression. Semi-structured interviews were guided by the Ottawa Decision Support Framework. Of 94 participants, 67 were uncertain about their decision. Common decisions identified were whether or not to take medications, attend support groups, undergo electroconvulsive therapy, and location of care. Those feeling certain were more likely to have made a decision (RR 1.37; 95% CI: 1.05, 1.78). However, 40 patients who had 'made a decision' in the recent past were uncertain about their decision. Compared with those who were certain, the uncertain group felt less informed (2.65 vs. 1.64; P < 0.001), less supported (2.63 vs. 1.88; P < 0.001) and less clear about how they valued the benefits and risks of options (2.57 vs. 1.69; P < 0.001). Other influential factors included concerns about confidentiality, distress from depression, embarrassment, panic attacks and lack of energy. Few patients wanted to defer decision making to their physician (n = 8) or family (n = 1). To support decision making, participants identified the need for: discussions with their psychiatrist, nurse or family doctor; access to printed information; and information provided by health professionals and health societies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".