Enhancing clinical practice in the management of distress: The Therapeutic Practices for Distress Management (TPDM) project
Bibliographic record
Abstract
OBJECTIVE: The Therapeutic Practices for Distress Management (TPDM) project was carried out to support clinicians in integrating recommendations from four clinical practice guidelines (CPGs) in routine care at five Pan Canadian cancer care sites. METHODS: Using a concurrent, mixed-method study design and knowledge translation (KT) activities, this project included two phases: phase I-a baseline/preparation phase and phase II-an intervention phase plus evaluation. The intervention phase (the focus of this report) included a one-year education and supervision program (24 hours in virtual class; 12-hour group supervision). Primary outcomes were knowledge and self-efficacy in practicing CPGs as measured by a Knowledge and Self-Efficacy Survey (KSES). A secondary outcome was observer-rated performances with standardized patients (objective structured clinical exams). Participants included 80 (90%) nurses, and 9 (10%) social workers (N = 89). RESULTS: The TPDM program was effective in accomplishing change in knowledge, self-efficacy, and performance. All measures demonstrated significant change pre and post module, with evidence of increasing knowledge (P < .01) and confidence (P < .01) over time. Further, there was evidence of a shift in barriers and enablers to practicing in alignment with the CPGs. CONCLUSIONS: A tailored education program using case-based learning and supervision over time improves knowledge and practice among front line clinicians. The findings have implications for quality improvement in cancer care.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".