Knowledge creation through total clinical outcomes management: a practice-based evidence solution to address some of the challenges of knowledge translation.
Bibliographic record
Abstract
INTRODUCTION: The challenges of knowledge translation in behavioural health care are unique to this field for a variety of reasons including the fact that effective treatment is invariably embedded in a strong relationship between practitioners and the people they serve. METHODS: Practitioners' knowledge gained from experience and intuition become an even more important consideration in the knowledge translation process since clinicians are, in fact, a component of most treatments. Communication of findings from science must be conceptualized with sensitivity to this reality. RESULTS: Considering knowledge translation as a communication process suggests the application of contemporary theories of communication which emphasize the creation of shared meaning over the transmission of knowledge from one person to the next. CONCLUSION: In this context outcomes management approaches to create a learning environment within clinical practices that facilitate the goals of knowledge transfer while respecting that the scientific enterprise is neither the sole nor primary repository of knowledge.
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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 | 0.237 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".