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Record W2588905814

Nurse Educators Collaborate in a Pan-Territorial Approach to Develop a Community Development Learning Opportunity

2016· article· en· W2588905814 on OpenAlexaffabout
Catherine Bradbury, Sue Starks, Kerry Lynn Durnford, Pertice Moffitt

Bibliographic record

VenueNorthern review · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsAurora CollegeYukon University
Fundersnot available
KeywordsIncentivePublic relationsAffect (linguistics)NursingCommunity developmentPolitical scienceBusinessSociologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Pan-territorial approaches are increasingly utilized to identify challenges and propose solutions in Canada’s North. This report describes a collaboration of nurse educators and stakeholders from all three territories to develop a self-directed learning module. Northern health and social service providers have the opportunity to support communities in building capacity to identify and address issues that affect their health and well-being. Recent evidence suggests that the majority of those providers have had little or no opportunity to acquire competencies in community development; this finding was the incentive for the community development project. While project goals were successfully achieved, the development team continues to reflect on influences of neo-colonialism and contextual factors, and the importance of embedding decolonizing practices in future initiatives.

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)0.015
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0060.004
Open science0.0020.015
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.096
GPT teacher head0.431
Teacher spread0.335 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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