Clinical research potential in <scp>V</scp>ictorian hospitals: the <scp>V</scp>ictorian clinician researcher needs analysis survey
Bibliographic record
Abstract
BACKGROUND: The 2012 McKeon Review highlighted the role of clinician researchers in patient based research and the need to foster this capacity. While anecdotal evidence suggests that clinician researchers are under threat and underfunded, Australian data on barriers and enablers of clinician-led research are scant. AIMS: To describe (i) characteristics of clinician researchers; (ii) for research-active clinicians: areas of research, barriers/enablers of research and factors associated with funding success; and (iii) for research-inactive clinicians: enablers of future research. METHODS: An online survey distributed through the Bio21 Cluster to clinicians (doctors, nurses, allied health professionals) in 15 Victorian hospitals between November 2011 and January 2012. RESULTS: Seven hundred and seventy of 1027 (75%) of respondents were research-active and were more likely to be male, medical doctors, aged 45-54 years, to work full-time and have a higher degree (all P < 0.01). Of clinicians with a higher degree, 28% were research-inactive. Clinicians identified protected research time (50%), designated research space (42%), clinical trial coordinators (35%), institutional funding (34%) and mentoring (33%) as critical enablers of research. Research-inactive clinicians identified protected research time as the key enabler of future research. CONCLUSIONS: To realise recommendations in the McKeon Review, hospitals and research bodies will need to protect research time and provide space and funding. Engaging research-inactive clinicians will build research 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 | 0.040 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".