MétaCan
Menu
Back to cohort
Record W2461129420 · doi:10.3928/01484834-20070601-05

Recruitment Strategies for Baccalaureate Nursing Students in Ontario

2007· article· en· W2461129420 on OpenAlexaffabout
Laureen Hayes

Bibliographic record

VenueJournal of Nursing Education · 2007
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNursingNursing shortageNurse educationUnit (ring theory)Economic shortageAttritionPsychologyMedicineGovernment (linguistics)

Abstract

fetched live from OpenAlex

This study explored the nature of recruitment practices for basic baccalaureate degree nursing programs in Ontario. Using a case study approach at three university sites, interviews of nursing faculty and institutional liaison officers were conducted, and recruitment publications and relevant institutional Web sites were examined. The findings show that nursing faculty members participate in student recruitment, but the recruitment activities are organized and carried out primarily at the institutional level to promote the university and its programs. Given the concerns regarding nursing shortages, recruitment objectives for nursing should reflect health system needs, as well as the needs of the individual institutions. Nursing faculty are in a favorable position to ensure nursing applicants receive information that is relevant and accurate to promote realistic expectations of nursing education and practice, which, in turn, may minimize student attrition and early withdrawal from nursing practice.

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)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.865

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0150.002
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.001
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 head0.104
GPT teacher head0.444
Teacher spread0.340 · 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 designObservational
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

Citations10
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nursing EducationSame topicNursing education and managementFrench-language works237,207