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Record W2067338029 · doi:10.5176/2315-4330_wnc14.64

Maintaining a Healthy Workforce

2014· article· en· W2067338029 on OpenAlexaffabout
Jean Chow, Particia M. Burrell, Ruth Grant Kalischuk, Ann Longnecker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of LethbridgeMount Royal University
Fundersnot available
KeywordsWorkforceComputer scienceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Abstract — The study describes the health maintenance practices and utilization of complementary and alternative therapies by nurses and nursing students from Canada, Hawaii and American Samoa. Forecasts of an ongoing nursing shortage coupled with a focus on healthy behavior provided an impetus to describe actual practices of self-care and use of alternative therapies. The study addresses the dearth of knowledge in this area. Nurses and students were asked to anonymously complete a two-part survey that enumerated their perspectives on alternative therapies usage and self-care. Indicators of self-care included sleep, nutrition, exercise, and time. Use of alternative therapies is an integral part of self-care for the nurses and students in the three study regions. Keywords-self-care; life style; nursing students; nurses; health I.

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)0.002
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.045
GPT teacher head0.346
Teacher spread0.301 · 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
GenreOther

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
Published2014
Admission routes2
Has abstractyes

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