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Record W4389953655 · doi:10.5539/jel.v13n1p51

Secondary Principals’ Perceptions and Practices for Implementing Student Suicide Prevention Programs

2023· article· en· W4389953655 on OpenAlexvenueno aff
Dawn Porter, Samantha H. Mullins

Bibliographic record

VenueJournal of Education and Learning · 2023
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingPsychologyMental healthSuicide preventionPhenomenology (philosophy)PerceptionPublic healthMedical educationPoison controlPedagogyPublic relationsNursingMedicinePolitical sciencePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

We explored secondary school principals’ knowledge of suicide prevention programs, their perceptions of the logistical and cultural barriers associated with suicide prevention program adoption, and their justification for adopting (or not adopting) suicide prevention programs in their schools. Principals, as positional leaders of schools, can lead the adoption and support of school-based suicide prevention programs for their students. Using a phenomenology framework, we conducted semi-structured interviews of eight secondary school principals working in public schools in the south-central United States. The principals readily identified the importance of supporting students’ mental health to enhance their learning as a justification for implementing suicide prevention programs for their students. They shared how limited staffing, time, perception of school responsibility for student mental health, and lack of knowledge of available suicide prevention resources were logistical, cultural, and knowledge barriers to adopting suicide prevention programs for students. Our research has profound implications for practice.

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)0.006
metaresearch head score (Gemma)0.017
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.111
GPT teacher head0.485
Teacher spread0.374 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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