Factors Influencing the Reporting of Notifiable Conduct in Health Professionals in Australia
Bibliographic record
Abstract
Objective: Notification of misconduct is a requirement by the Australian Health Practitioner Regulatory Agency. The study focuses on examining the factors that influence the intention to report misconduct by applying the Theory of planned behavior model. Method: The quantitative online survey study using vignettes and questionnaires examined one hundred and seventy-two regulated health professionals on factors that may impact the willingness to report on notifiable conduct. Results: The findings indicate that clinicians were more inclined to report on sexual misconduct and alcohol misuse conduct, however, they did not report on clinicians’ competencies. Perceived behavior control, descriptive norms, and subjective norms predicted intention to report notifiable conduct, while attitude was not a predictor. Clinicians with a higher reporting intention were more likely to engage in actual reporting behavior. The behavioral pattern of reporting notifiable conduct did not differ among the three health professional groups. Conclusion: The findings identify important factors that assist clinicians in their decision-making when reporting observed misconduct. Awareness of these factors reduces health care related misconduct. That is, organizations are encouraged to develop specific programs that facilitate clinicians’ decision making by educating and refreshing their knowledge of the factors impacting their intention to report misconducts.
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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 | 0.008 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".