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Record W1565726123 · doi:10.1071/ah060298

Predictors of failure by medical practitioners to report suspected child abuse in Queensland, Australia

2006· article· en· W1565726123 on OpenAlexaboutno aff
Robert Schweitzer, Lisa Buckley, Paul Harnett, Natalie J. Loxton

Bibliographic record

VenueAustralian Health Review · 2006
Typearticle
Languageen
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsNeglectJudgementChild abuseMedicineQuarter (Canadian coin)Family medicinePsychologyPsychiatrySuicide preventionMedical emergencyPoison controlPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.343
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2006
Admission routes1
Has abstractyes

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