Exploring the research culture in the health information management profession in Australia
Bibliographic record
Abstract
BACKGROUND: Research is an important activity that informs knowledge and practice. The research culture within the Australian Health Information Management (HIM) profession has not been previously reported. OBJECTIVE: This study explored the perceptions of HIM practitioners about research in their role to establish if there is a research culture in the Australian HIM profession. METHOD: An online survey was distributed to the HIM community using a snowball recruitment strategy. RESULTS: Of the 149 respondents, more than half (54%) identified they possessed research skills from prior education, whilst 40% considered they had a strong knowledgebase in conducting research. However, only a quarter of respondents indicated that they should undertake research in their role. Barriers to undertaking research included recognition, organisational support and time. DISCUSSION: The findings from this study reflected other studies within clinical workforces. The lack of recognition and support to incorporate research into practitioner roles has implications for the profession and its body of knowledge. CONCLUSION: Advocating for research to be incorporated into practitioner roles is required to inform knowledge and practice. Increased professional development opportunities may create a stronger research culture within the HIM profession in Australia and strengthen the position of the profession within health.
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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 | 0.072 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".