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Record W2102686180 · doi:10.1177/1059840510368801

A Strengths-Based Group Program on Self-Harm

2010· article· en· W2102686180 on OpenAlexafffund
Margaret McAllister, Penelope Hasking, Andrew Estefan, Kerry McClenaghan, John B. Lowe

Bibliographic record

VenueThe Journal of School Nursing · 2010
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsHarmSAFERFocus groupInclusion (mineral)Medical educationPsychologyNursingMedicineSocial psychologyBusinessComputer securityComputer science

Abstract

fetched live from OpenAlex

Every day in Queensland, Australia, student services within schools are responding to children who have deliberately self-injured. Although school nurses are in a prime position to effectively intervene, mitigate risk, and promote healthy self-caring behaviors, no programs that focus specifically on self-harm currently exist. This feasibility study of a program to assist young people find safer alternatives to self-harm canvassed opinions of 12 school nurses in secondary schools on the Sunshine Coast in 2009. Participants showed strong support and reported that the program was much needed; had an innovative, strengths-oriented approach; incorporated an essential training component; would likely be engaging for young people; and was in appropriate format. Perceived challenges to implementation included garnering support from the school community and educational stakeholders and recruiting young people most likely to benefit. Suggested changes included providing a youth-friendly name for the program and formalizing inclusion criteria to select appropriate group members.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.474
Teacher spread0.425 · 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 designObservational
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

Citations15
Published2010
Admission routes2
Has abstractyes

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