Rethinking Health Professionals’ Motivation to Do Research: A Systematic Review
Bibliographic record
Abstract
Background: Health professionals’ engagement in translational health and medical research (HMR) is fundamental to evidence-based practice leading to better patient health outcomes. However, there is a decline in the number of health professionals undertaking research which has implications for patient health and the economy. Informed by the motivation-based expectancy-value-cost (EVC) and self determination theories (SDT), this systematic literature review examined the barriers and facilitators of health professionals’ (HPs) motivation to undertake research. Methods: The literature was searched between 2011 and 2021 for relevant peer-reviewed articles written in English, using CINAHL Complete, Informit, Medline Ovid, Medline (PubMed), Scopus, Web of Science and Google Scholar databases. This systematic review was performed and reported in accordance with the PRISMA guidelines. Results: Identified barriers to HPs’ engagement with research included the lack of knowledge, skills, and competence to conduct research, lack of protected research time, lack of funding and lack of organisational support. Integration of the findings of this review based on the EVC and SDT theories indicate that research capacity, ie, expectancy and competence is highly influenced by attitude, ie, the type of value (attainment, intrinsic or utility) and connection attributed to research. HPs who had very positive attitude towards research demonstrated all three values and were keen to take up research despite the barriers. Those who had a positive attitude were only motivated to do research because of its utility value and did not necessarily see it as having personal relevance for themselves. HPs who were unmotivated did not see any personal connection or relatedness to the research experience and saw no value in research. Conclusion: The attitude HPs hold in their value of research is a catalyst for motivation or amotivation to engage in research as it directly influences the relevance of barriers. Facilitators that expedite the research journey have been attributed to research training, mentorship programs and supportive organisational research culture. Motivation of HPs explored through EVC and SDT is critical to the maintenance of a research culture and the clinician-researcher development pipeline.
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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 | 0.077 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".