The Clinician Scientist: How Rehabilitation Fares ―A Scoping Review
Bibliographic record
Abstract
<i>Background</i>: Clinician scientists (CS) play a role in bridging the gap between research and practice. However, the role of a CS is less established for healthcare professionals in rehabilitation in comparison to medicine. <i>Objective</i>: The purpose of this scoping review was to explore different roles and models of a clinician scientist in rehabilitation and compare this to medicine and nursing. <i>Methods</i>: This review was structured according to the Arksey and O’Malley (2005) framework for scoping reviews. A literature search was conducted from the following databases: EMBASE, MEDLINE, AMED and Web of Science; a grey literature search was conducted from MacSphere, ProQuest, Duck DuckGo, and Google. <i>Results</i>: 95 articles met the inclusion criteria with 73 studies in medicine, including nursing, 10 articles from rehabilitation and 12 articles with mixed professions. The main barriers identified for rehabilitation involved time constraints and lack of funding for research, whereas primary facilitators included development of formalized training programs and presence of mentorship programs. <i>Conclusion</i>: The role of the clinician scientist is more established in medicine compared to rehabilitation. There is a need for an established career trajectory accompanied with training programs. Further studies are required to shape the role and development of secure funding models for CS positions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".