Challenges in conducting research on collaborative mental health care: a qualitative study
Bibliographic record
Abstract
BACKGROUND: We sought to understand poor uptake of the Primary Care Assessment and Research of a Telephone Intervention for Neuropsychiatric Conditions with Education and Resources study (PARTNERs), a pragmatic randomized controlled trial of a collaborative care intervention for people experiencing depression, anxiety or at-risk drinking. We explored primary care providers' experience with PARTNERs, and preferences regarding collaborative care models and trials. METHODS: In this qualitative study, we interviewed primary care providers across Ontario who had participated in PARTNERs, using stratified sampling to reach high-, low- and nonreferring providers in urban and rural settings. We audio-recorded, transcribed and thematically analyzed the interviews between May and December 2017, collecting and analyzing data concurrently until achieving saturation. RESULTS: We interviewed 23 primary care providers. They valued the unique availability of telephone-based coaching for patients but desired greater integration of the coach into their practice. They appreciated expert psychiatric recommendations but rarely changed their practices. Sites varied in organizational adoption and implementation of the study, including whether they designated a local champion, proactively identified eligible patients, integrated the study into existing workflows and reflected on (and revised) practices. These behaviours affected continuing awareness of the study and referral rates. INTERPRETATION: Study uptake was influenced by the limited relationship between PARTNERs coaches and primary care providers, and variable attention to leadership, training and quality improvement as vital elements of collaborative care. Study designs focusing on implementation could promote reach and penetration of novel interventions in the practice setting and more successfully advance collaborative care implementation.
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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 | 0.096 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".