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Record W1999640456 · doi:10.12927/cjnl.2004.16245

Maximizing the Involvement of Rural Nurses in Policy

2004· review· en· W1999640456 on OpenAlexaffvenueabout
Judith C. Kulig, Deana Nahachewsky, Elizabeth Thomlinson, Martha MacLeod, Fran Curran

Bibliographic record

VenueNursing leadership · 2004
Typereview
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPosition (finance)Rural managementRural healthNursingContext (archaeology)Work (physics)Health policyRural areaPolicy developmentPublic relationsPolitical scienceBusinessEconomic growthMedicineRural developmentPublic healthPublic administration

Abstract

fetched live from OpenAlex

Rural health issues are increasingly recognized as needing attention, but many health policies in Canada are developed for the urban context and universally applied to rural settings. Addressing rural nurses' opportunities for involvement in policy will contribute to our general understanding of rural health while improving community health services. Rural nurses are in a unique position to assist because of their intimate knowledge of their communities and their position as informal community leaders. Challenges to their involvement include decreased numbers and lack of educational preparation about policy. A strength is the higher percentage of rural nurses who are managers compared to their urban counterparts. Nursing education programs need to include theoretical content and practical opportunities related to health policy. Managers need to support rural nurses' attempts in policy development by providing opportunities for membership on policy committees. Finally, once obtaining skills in the policy arena, rural nurses need to work within their communities and workplaces to help develop and implement more appropriate rural-based policies.

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)0.007
metaresearch head score (Gemma)0.011
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.234
GPT teacher head0.403
Teacher spread0.168 · 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
GenreReview

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

Citations13
Published2004
Admission routes3
Has abstractyes

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