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Record W2076682540 · doi:10.1177/0829573506298471

“I Am Not Well-Equipped”

2006· article· en· W2076682540 on OpenAlexaff
Nancy L. Heath, Jessica R. Toste, Erin L. Beettam

Bibliographic record

VenueCanadian Journal of School Psychology · 2006
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyConstruct (python library)PerceptionKnowledge levelQualitative analysisQualitative researchSocial psychologyMedical educationMathematics educationMedicineSociology

Abstract

fetched live from OpenAlex

Fifty teachers completed a survey to investigate knowledge, self-perceived knowledge, and attitudes regarding self-injury (SI). Teachers were aware of basic facts concerning SI; however, 78% underestimated prevalence, and only 20% felt knowledgeable. Attitudes were mixed, with 48% finding the idea of SI horrifying; however, 68% disagreeing that SI was “often manipulative.” Principal components analysis indicated that perceived knowledge emerged as a separate construct from attitude s toward SI. Years of teaching experience was related to self-perceived knowledge, but not to attitudes. In addition, 74% of teachers reported having a personal encounter with SI, and 62% felt that SI is increasing in the schools. Qualitative analysis of open-ended questions revealed a strong desire for further knowledge and training. Results indicate that teachers’ perceptions are not consistent with studies of SI in clinical settings but are consistent with recent research in community and school samples. The need for teacher education about SI is emphasized.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.041
GPT teacher head0.332
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations82
Published2006
Admission routes1
Has abstractyes

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