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Record W4254724360 · doi:10.32920/ryerson.14645283.v1

Identifying Mental Health Initiatives for Independently Funded Christian Elementary Schools in Ontario

2021· preprint· en· W4254724360 on OpenAlexaffabout
Margaret Kloet

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMental healthPlan (archaeology)Variety (cybernetics)Implementation researchProcess (computing)Qualitative researchPsychologyMedical educationPublic relationsPolitical sciencePsychological interventionSociologyComputer scienceMedicineGeographySocial sciencePsychiatry

Abstract

fetched live from OpenAlex

This study seeks to understand the use of mental health strategies within independently funded Christian elementary Schools (IFCES), while considering how research literature identifies the implementation of mental health strategies in publicly funded elementary schools. There is a major research gap for IFCES in this area. This study used a qualitative approach via structured interviews to gather data reflecting how IFCES provide mental health supports. A wide variety of programming and supports within the schools (both IFECS and publicly funded) were identified during the research process. While the tiered system of support has been strongly considered in research literature (Sanchez, Cornacchio, Poznanski, Golik, Chou, & Comer, 2018), the IFECS sector did not intentionally use this framework as a support to implement a mental health plan. This study identified that IFECS are gaining momentum in mental health programming and would benefit further from utilizing a framework to address their growing mental health needs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.286
Teacher spread0.228 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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