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Is elder abuse on the curriculum? The relative contribution of child abuse, domestic violence and elder abuse in social work, nursing and medicine qualifying curricula

2007· article· en· W1988788822 on OpenAlexaff
Paul KingstonC, Bridget Penhale, Gerry Bennett

Bibliographic record

VenueHealth & Social Care in the Community · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsElder abuseNeglectCurriculumDomestic violenceChild abuseSocial workNursingMedicinePoison controlSuicide preventionMedical educationPsychologyPsychiatryPedagogyMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

In order to have a sufficiently high index of suspicion about elder abuse and neglect, child abuse and domestic violence, a knowledge base about the phenomena is clearly necessary. Education in the widest sense for health and social care professionals about elder abuse and neglect, child abuse and domestic violence is increasing with regular journal articles, media coverage and modular coverage now found on post basic training courses for health and social care professionals. However, it is suggested that the relative importance of a topic can be judged by its importance in the basic curricula of medical, nursing and social work qualifying courses. A survey of all medical schools and colleges, nursing colleges and university departments with qualifying studies in social work was conducted between April-September 1994. The aim was to ascertain the relative educational content in the curricula pertaining to elder abuse and neglect, child abuse, domestic violence, and generic family violence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.049
GPT teacher head0.411
Teacher spread0.362 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainIncentives
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

Citations13
Published2007
Admission routes1
Has abstractyes

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