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Record W3109588744 · doi:10.5334/ijic.4709

Distributive Leadership Within an Emerging Network of Integrated Youth Health Centres: A Case Study of Foundry

2020· article· en· W3109588744 on OpenAlexaffabout
Amy Salmon, Saranee Fernando, Mai Berger, Karen Tee, Kristy Gerty, Warren Helfrich, P. Liversidge

Bibliographic record

VenueInternational Journal of Integrated Care · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSpinal Cord Injury BCCentre for Advancing Health Outcomes
Fundersnot available
KeywordsFacilitatorContext (archaeology)Leadership developmentIntegrated careHealth careKnowledge managementNursingSociologyPublic relationsBusinessPsychologyMedicinePolitical scienceComputer scienceEconomic growthSocial psychologyEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: , a network of integrated youth health centres in British Columbia, Canada, and explores the function and impact of distributive leadership in the context of a large-scale effort towards integrated service delivery for youth experiencing mental health and substance use challenges. METHODS: Qualitative data was obtained from a developmental evaluation of Foundry using a longitudinal, ethnographic approach. Over 150 participants involved in the development of six Foundry centres were interviewed individually or in focus groups. Purposive and theoretical sampling strategies were used to maximize the diversity of perspectives represented in the data set. RESULTS AND DISCUSSION: Distributive leadership was observed to be a facilitator for achieving service and system-level integration. Distributive leadership was effective in promoting streamlined service provision, and coordinating efforts towards optimized access to care. A new culture of leadership emerged through collaboration and relationship-building based on a common value system to prioritize youth needs. CONCLUSION: As Foundry, and other integrated youth services, continues to expand, distributive leadership shows promise in assuring diverse and coordinated input for integrating services.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.553
GPT teacher head0.593
Teacher spread0.040 · 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 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

Citations7
Published2020
Admission routes2
Has abstractyes

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