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Record W4220657296 · doi:10.12927/cjnl.2022.26752

Influencing Work Culture: A Strengths-Based Nursing Leadership and Management Education Program

2022· article· en· W4220657296 on OpenAlexaffvenue
Pam Hubley, Laurie N. Gottlieb, M. J. Durrant

Bibliographic record

VenueNursing leadership · 2022
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsSeneca PolytechnicMcGill University Health CentreHospital for Sick Children
Fundersnot available
KeywordsWork (physics)Health careNursingOrganizational culturePsychologySample (material)Strengths and weaknessesMedical educationPublic relationsMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Little is understood about developing the capacity of healthcare leaders to influence work cultures that promote health and healing. A program designed for clinical leaders to teach them how to create Strengths-Based care environments was piloted and evaluated using mixed methods. Data were collected from a convenience sample of 15 participants from two clinical sites. Evaluation of the data revealed that the program was impactful and that participants had the impetus to influence work environments by shifting their discourse from traditional deficit models of care toward an approach that illuminates a focus on strengths and relational ways of being a leader.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.093
GPT teacher head0.345
Teacher spread0.251 · 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.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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