Bibliographic record
Abstract
BACKGROUND: Partnerships in occupational therapy between practitioners and researchers are important to advance knowledge relevant to clinical practice and support evidence-based practice in the profession. PURPOSE: This Muriel Driver lecture discusses why we should support practitioners' engagement in research and examines essential conditions required for successful collaborative partnerships in research. KEY ISSUES: Collaborative partnerships can alleviate the challenges preventing practitioners from participating in research and ensure that research initiatives are more relevant to clinical practice. Key factors for building and sustaining meaningful partnerships include the presence of favourable pre-partnership conditions related to the context and the use of guiding principles focusing on vision, values, trust, communication, power sharing, and interactions. IMPLICATIONS: Several of the factors found to foster a good collaborative partnership are consistent with our core competency roles and reflect our professional values. Being mindful of these factors when initiating research collaborations would increase the likelihood of success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.137 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.085 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.004 | 0.043 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".