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Record W2779556546 · doi:10.1080/23303131.2017.1421284

Leadership development in human services: Variations in agency training, organizational investment, participant satisfaction, and succession planning

2017· article· en· W2779556546 on OpenAlexaffabout
Rosemary Vito

Bibliographic record

VenueHuman Services Organizations Management Leadership & Governance · 2017
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsThe King's University
Fundersnot available
KeywordsSuccession planningAgency (philosophy)Context (archaeology)Investment (military)Public relationsBusinessKnowledge managementPsychologyManagementPolitical scienceSociologyEconomicsGeography

Abstract

fetched live from OpenAlex

The need for agency investment in leadership development is acute, given rising organizational complexity and anticipated management retirements. Using a multiple case study, multiple methods design, this article compares qualitative findings on the varying approaches to leadership development, organizational context, training outcomes, and succession planning in two children’s mental health agencies in Ontario, Canada. Key findings are highlighted: formal training and informal learning opportunities; extent of organizational investment, internal and external agency pressures; mixed outcomes regarding participant satisfaction, learning, and practice application; and lack of agency/sector succession planning. Conceptual and practical implications for agency leaders and future researchers are highlighted.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.556
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.179
GPT teacher head0.350
Teacher spread0.171 · 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 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

Citations28
Published2017
Admission routes2
Has abstractyes

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