Professionals’ beliefs about indicators of child sexual abuse
Bibliographic record
Abstract
The objective of the study described here was to obtain information on the beliefs of professionals concerning possible indicators of a child having been sexually abused. Data were collected by means of self-administered questionnaires, distributed at meetings on child sexual abuse. The respondents were professionals working in the field of child sexual abuse. Twenty-three social workers, 14 psychologists, 12 nurses, eight medical doctors, including three paediatricians and three psychiatrists, three policemen and two nurses participated. The variables studied were the perceived prevalence of sexual abuse in British children under the age of 10 and the perceived prevalence of various possible signs of sexual abuse in two sub-groups of these children: those who had been sexually abused, and those who had not. The signs investigated were: behavioural problems, somatic complaints, enuresis, chronic urinary infections, fear of toileting, sexualized language, sexualized behaviour, comments suggestive of sexual activity, allegations of sexual abuse, sexually transmitted diseases and reflex anal dilatation. The results indicate that these professionals have widely differing beliefs about signs which might indicate that a child has been sexually abused. We conclude that data on the prevalence of indicators of child sexual abuse are essential to ensure that appropriate informed and consistent decisions can be made.
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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 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".