Predictors of failure by medical practitioners to report suspected child abuse in Queensland, Australia
Bibliographic record
Abstract
OBJECTIVE: The goal of this investigation was to examine the level of notification of child abuse and neglect and the perceived deterrents to reporting by medical practitioners, who are mandated to report their suspicions but might choose not to do so. DESIGN: A random sample of medical practitioners was surveyed. About three hundred medical practitioners were approached through the local Division of General Practice. 91 registered medical practitioners in Queensland, Australia, took part in the study. RESULTS: A quarter of medical practitioners admitted failing to report suspicions, though they were mostly cognisant of their responsibility to report suspected cases of abuse and neglect. Only the belief that the suspected abuse was a single incident and unlikely to happen again predicted non-reporting (chi(2) [1, N = 89] = 7.60, p < 0.01). No gender, age or parent status differences were found between reporters and non-reporters. CONCLUSIONS: Although the rate of non-reporting shows improvement from previous research, it is still at an unacceptable level. The failure to report appears to result not from judgement about the presence or absence of indicators of child abuse and neglect but a threshold that moves individuals to act on their suspicions. Professional development should focus on some of the fallacies which often influence medical practitioners' decisions.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".