Primary Care Provider Views About Usefulness and Dissemination of a Web-Based Depression Treatment Information Decision Aid
Bibliographic record
Abstract
BACKGROUND: Decisions related to mental health are often complex, problems often remain undetected and untreated, information unavailable or not used, and treatment decisions frequently not informed by best practice or patient preferences. OBJECTIVE: The objective of this paper was to obtain the opinions of health professionals working in primary health care settings about a Web-based information decision aid (IDA) for patients concerning treatment options for depression and the dissemination of the resources in primary care settings. METHODS: Participants were recruited from primary care clinics in Winnipeg and Ottawa, Canada, and included 48 family physicians, nurses, and primary care staff. The study design was a qualitative framework analytic approach of 5 focus groups. Focus groups were conducted during regular staff meetings, were digitally recorded, and transcripts created. Analysis involved a content and theme analysis. RESULTS: Seven key themes emerged including the key role of the primary care provider, common questions about treatments, treatment barriers, sources of patient information, concern about quality and quantity of available information, positive opinions about the IDA, and disseminating the IDA. The most common questions mentioned were about medication and side effects and alternatives to medication. Patients have limited access to alternative treatment options owing to cost and availability. CONCLUSIONS: Practitioners evaluated the IDA positively. The resources were described as useful, supportive of providers' messages, and accessible for patients. There was unanimous consensus that information needs to be available electronically through the Internet.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".