MétaCan
Menu
Back to cohort
Record W1971200856 · doi:10.1108/13660750110402626

Leadership in uncharted territory: developing the role of professional practice leader

2001· article· en· W1971200856 on OpenAlexaff
Mary Beth Bezzina, Linda B. Fischer, Leslie A. Harden, Karen Perkin, Danya Walker

Bibliographic record

VenueLeadership in Health Services · 2001
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsSt Joseph's Health Care
Fundersnot available
KeywordsCoachingAccountabilityHealth carePublic relationsProfessional developmentWork (physics)PsychologyLeadership developmentMedical educationBusinessPolitical scienceMedicinePedagogyEngineering

Abstract

fetched live from OpenAlex

Shows how a new role, professional practice leader (PPL), developed as a health care organization shifted from department‐based hierarchical management to team‐based management. The work of PPLs in this organization is to provide leadership within a shared decision making, outcome‐focused environment. PPLs play an integral role in developing and supporting clinical decision‐making structures. They provide corporate leadership in setting guidelines for professional practice and in promoting quality practice in budget and strategic planning. In an evolving organization, they maintain focus on clarifying and understanding professional and organizational accountability. PPLs support and facilitate professional development and clinical education. They provide external links with other health care organizations, educational institutions and community partners. This role is relationship‐oriented and involves both doing and teaching the skills of facilitating, mentoring and coaching. Developing a new role involves establishing new values and forging collaborative mutually rewarding relationships.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.011
Scholarly communication0.0070.007
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.430
GPT teacher head0.473
Teacher spread0.043 · 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 designNot applicable
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

Citations2
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueLeadership in Health ServicesSame topicHealthcare Quality and ManagementFrench-language works237,207