Research culture in allied health: a systematic review
Bibliographic record
Abstract
Research evidence is required to guide optimal allied health practice and inform policymakers in primary health care. Factors that influence a positive research culture are not fully understood, and nor is the impact of a positive research culture on allied health professionals. The aim of this systematic review was to identify factors that affect allied health research culture and capacity. An extensive search of 11 databases was conducted in June 2015. Studies were included if they were published in English, had full-text availability and reported research findings relating to allied health professions. Study quality was evaluated using the McMaster Critical Review Forms. Fifteen studies were eligible for inclusion. A meta-analysis was not performed because of heterogeneity between studies. Allied health professionals perceive that their individual research skills are lower in comparison to their teams and organisation. Motivators for conducting research for allied health professionals include developing skills, increasing job satisfaction and career advancement. Barriers include a lack of time, limited research skills and other work roles taking priority. Multilayered strategies, such as collaborations with external partners and developing research leadership positions, aimed at addressing barriers and enablers, are important to enhance allied health research culture and capacity.
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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 | 0.033 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".