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Record W3082943631 · doi:10.1177/0840470420952472

The strategic leadership of nursing directorates in the context of healthcare system reform

2020· article· en· W3082943631 on OpenAlexaffabout
Marcela Ferrada-Videla, Sylvie Dubois, Jacinthe Pépin

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversité de MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsCredibilityCognitive reframingStrategic planningContext (archaeology)NursingHealth careStrategic leadershipFocus groupCorporate governanceOrganizational culturePublic relationsPsychologyManagementPolitical scienceBusinessMedicineMarketingSocial psychology

Abstract

fetched live from OpenAlex

In Quebec, the strategic leadership of nursing directorates remains poorly documented despite its importance for the performance of their organizations. Using three focus groups and 31 individual semistructured interviews, a qualitative descriptive study was conducted, including 35 participants from 18 of the Quebec 34 health institutions created in 2015 by the last reform. Seven themes emerged: (1) taking ownership of the strategic positioning, (2) developing and communicating a vision, (3) making strategic, systematic, and measured choices, (4) reframing roles, (5) getting involved in the strategic decision-making processes, (6) developing the political capacity, and (7) building alliances. Four professional and organizational components influenced the nursing directorates' leadership capacity: clinical credibility, a sufficient number of people educated at the graduate level, organizational culture, and size of the institution. It is expected that these results regarding nursing directorates' exercise of strategic leadership will lead to better governance and quality of nursing care.

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)0.009
metaresearch head score (Gemma)0.010
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 score0.498
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.013
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.385
GPT teacher head0.459
Teacher spread0.073 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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