MétaCan
Menu
Back to cohort
Record W1972928309 · doi:10.12927/cjnl.2012.22815

The Research to Action Project: Applied Workplace Solutions for Nurses

2012· article· en· W1972928309 on OpenAlexaffvenueabout
Linda Silas

Bibliographic record

VenueNursing leadership · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsCanadian Nurses Association
Fundersnot available
KeywordsOvertimeAbsenteeismGeneral partnershipNursing shortageNursingEconomic shortageProfessional developmentPolitical scienceMedicinePublic relationsPsychologyMedical educationNurse educationGovernment (linguistics)

Abstract

fetched live from OpenAlex

The number of new nurses entering the profession has increased, but the need to retain nurses in the profession continues to be a critical priority. The consequences of the nursing shortage are reflected in continued high levels of overtime, absenteeism and turnover. The Canadian Federation of Nurses Unions (CFNU), in partnership with the Canadian Nurses Association, the Canadian Healthcare Association and the Dietitians of Canada, initiated the project Research to Action: Applied Workplace Solutions for Nurses (RTA). The RTA initiative comprised research-based pilot projects, implemented in 10 jurisdictions across the country, that aimed to improve workplaces and increase the retention and recruitment of nurses. Unions, employers, governments, universities and professional associations came together in an unprecedented show of collaboration. Lessons and knowledge were shared among the projects, which were evaluated for their viability in other jurisdictions and professions. The pilots led to increased leadership, engagement and professional development, and decreased overtime, absenteeism and turnover.

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)0.031
metaresearch head score (Gemma)0.021
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 score0.031
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0020.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.003

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 head0.741
GPT teacher head0.585
Teacher spread0.156 · 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

Citations3
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueNursing leadershipSame topicGlobal Health Workforce IssuesFrench-language works237,207