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Record W1564455076 · doi:10.22230/ijepl.2014v9n7a516

School Mental Health: The Impact of State and Local Capacity-Building Training

2014· article· en· W1564455076 on OpenAlexvenueno aff
Sharon H. Stephan, Carl E. Paternite, Lindsey Grimm, Laura Hurwitz

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2014
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCapacity buildingMental healthScale (ratio)Baseline (sea)Medical educationService (business)Training (meteorology)PsychologyPolitical scienceMedicineNursingPublic relationsBusinessMarketingGeographyPsychiatry

Abstract

fetched live from OpenAlex

Despite a growing number of collaborative partnerships between schools and community-based organizations to expand school mental health (SMH) service capacity in the United States, there have been relatively few systematic initiatives focused on key strategies for large-scale SMH capacity building with state and local education systems. Based on a framework of ten critical factors for capacity building, as well as existing best practices, two case studies were utilized to develop a replicable capacity-building model to advance interagency SMH development. Seventy education and mental health stakeholders from two selected states participated in baseline assessments of skill com-petency and critical factor implementation followed by two-day trainings (one in each state); 29 (41%) of the participants also completed a six month follow-up assessment. Targeted competencies increased significantly for participants from both states, with large effect sizes (d = 2.05 and 2.56), from pre- to post-training. Participant reports of critical factor implementation increased significantly for one of the two states (t[15] = -6.40, p < .001, d = 1.77). Results inform specific training recommendations for stakeholders and collaborative teams, as well as policy implications to support future development of SMH service capacity.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.301
GPT teacher head0.512
Teacher spread0.211 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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