Barriers that inhibit nurses reporting suspected cases of child abuse and neglect
Bibliographic record
Abstract
Objective: An integrative review of the literature was undertaken to identify barriers that inhibit nurses from reporting suspected cases of child abuse and neglect. Primary Argument: Nurses in all states and territories of Australia except Western Australia are legally required to report suspicions of child abuse and neglect to relevant child protection services. Nurses often have first contact with abused children, yet they do not make the top five list of people who notify. There is limited evidence on what motivates the reporting process and it appears that while nurses are in a key position to report suspected cases of abuse, barriers may exist that hinder this process. These barriers must be identified and addressed. Findings: Limited education on recognising signs and symptoms of abuse was found to be a major barrier to reporting. Other barriers include limited experience, poor documentation, low opinion of child protection services, fear of perceived consequences, and lack of emotional support for nurses through the reporting process. Conclusion: Although nurses are mandatory notifiers; that is, they are required by law to report child abuse and neglect, education in this area is not compulsory. While most Australian nursing degrees provide some content on child abuse and neglect, this is not a legal requirement nor is the content standardised. The introduction of compulsory mandatory reporting education should be considered for all undergraduate and post graduate nurses. Further research is needed to evaluate the effects of mandatory reporting education on outcomes and to reduce identified barriers to reporting. This in turn may offer greater protection for children, the most vulnerable members of society.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".