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Record W2706281083 · doi:10.12927/cjnl.2017.25110

Leadership Perspective: Bringing Nursing Back to the Future Through People-Powered Care

2017· article· en· W2706281083 on OpenAlex
Shirlee Sharkey, Nancy Lefebre

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueNursing leadership · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsPublic Health OntarioUniversity of TorontoCanadian Nurses Association
Fundersnot available
KeywordsPerspective (graphical)NursingPsychologyNurse AdministratorSociologyMEDLINEMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

At a time when there is a growing interest in person- and family-centred care and integrated community-based models, the unique strengths and expertise of home care nursing is a strategic lever for change across all healthcare settings. In this paper, we explore the theme of people-powered care as a universal starting point - a new approach to health and wellness that is anchored in the strengths of people, their networks and the patterns of everyday life. Leveraging key insights from home and community care, along with broader societal shifts towards personalization and empowerment, we discuss how nurses in all areas of the system can lead the way by empowering staff, patients and their families. Finally, we look at the implications for nursing leadership including how our knowledge, skills and abilities must continue to evolve to effectively impact change and enable this vital transformation to occur.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0030.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.150
GPT teacher head0.303
Teacher spread0.154 · 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