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Record W2342445740 · doi:10.14288/1.0053947

Evaluation of an intensive group-process based model of team leadership development: implications for Canadian health care employees

2009· article· en· W2342445740 on OpenAlexaboutno aff
Timothy G. Black

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Health careProcess managementGroup (periodic table)PsychologyGroup developmentNursingKnowledge managementOperations managementBusinessMedicineComputer sciencePolitical scienceSocial psychologyEngineering

Abstract

fetched live from OpenAlex

The traditional model of leadership in medicine and health care generally centres around a hierarchical structure of power and influence, resting in the hands of a select few administrators, with limited input from employees. A newly developed Cancer treatment centre in the Province of British Columbia, Canada has attempted to institute a unique, team-based system of shared leadership and decision-making. In order to accomplish this task, the Senior Administrator of the centre hired professional group development experts to facilitate the formation of the newly established Leadership Team. A team of nine individuals participated in a group-process based model of team leadership development, consisting of a series of intensive weekend workshops. This study evaluates the impact of those intensive workshops on the members of the Cancer centre Leadership Team. Qualitative case-study methodology, combined with the use of indepth interviews, illuminated eight categories of shared experience among seven of the nine team members, as a result of having participated in the workshop series.

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)0.019
metaresearch head score (Gemma)0.026
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.450

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0080.003
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0010.002
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 head0.062
GPT teacher head0.237
Teacher spread0.175 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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