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Record W2911908317 · doi:10.22230/ijepl.2019v14n5a864

Coaching as a Knowledge Mobilization Strategy: Coaches' Centrality in a Provincial Research Brokering Network

2019· article· en· W2911908317 on OpenAlexaffvenueabout
Joelle Rodway

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCentralityCoachingMental healthPublic relationsSocial network analysisPsychologyPolitical scienceSociologySocial scienceSocial capital

Abstract

fetched live from OpenAlex

Ontario’s Child and Youth Mental Health (CYMH) program is a provinciallysponsored initiative that aims to build school district capacity for developing research-informed school mental health policies. This article reports findings from a mixed-methods study that employs social network theory and analysis tools to explore the centrality of CYMH coaches within this research brokering network. Overall, CYMH coaches are central within these social networks, although the patterns of interaction differ from the program’s original design, with some coaches being more central than others. While formal CYMH professional development events appear to be the most direct approach to connecting research, policy, and practice, informal social networks provide the support necessary to make sense of research-based materials for use in local policymaking.

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)0.017
metaresearch head score (Gemma)0.054
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.054
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0220.010
Scholarly communication0.0100.004
Open science0.0030.016
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.774
GPT teacher head0.698
Teacher spread0.076 · 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

Citations10
Published2019
Admission routes3
Has abstractyes

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