MétaCan
Menu
Back to cohort
Record W1923832236 · doi:10.12927/cjnl.2015.24353

Recruitment and Retention in Rural Nursing: It's Still an Issue!

2015· article· en· W1923832236 on OpenAlex

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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNursing leadership · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Northern British ColumbiaAurora CollegeHôpital Maisonneuve-RosemontUniversity of Lethbridge
FundersCanadian Institutes of Health Research
KeywordsVariety (cybernetics)IncentiveNursingHealth careHealthcare deliveryRural areaPublic relationsBusinessMedicinePolitical science

Abstract

fetched live from OpenAlex

A perennial issue for rural and remote communities in Canada and in other parts of the world is access to a healthcare delivery system including healthcare personnel to provide care to their residents. In total, 18% of Canadians live in rural locations but by proportion have fewer healthcare providers compared with urban settings. Relying on a recently completed documentary analysis of published reports and grey literature on rural and remote nursing practice from Canada and around the world, we recognize that recruitment and retention will be a recurring issue. However, a variety of programs and initiatives have been developed to address this age-old problem. A discussion is provided about educational opportunities, financial incentives and enhanced infrastructure that have been developed to address recruitment and retention challenges. Ongoing evaluations of each of these areas are necessary but require cooperation across provincial and national settings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.620
GPT teacher head0.511
Teacher spread0.109 · 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