MétaCan
Menu
Back to cohort
Record W2028541699 · doi:10.12927/hcpol.2009.20817

The Ontario New Graduate Nursing Initiative: An Exploratory Process Evaluation

2009· article· en· W2028541699 on OpenAlexaffvenueabout
Janice J. Beaty, Wendy Young, Marlene Slepkov

Bibliographic record

VenueHealthcare policy · 2009
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsCanadian Celiac Association
Fundersnot available
KeywordsProcess (computing)NursingNursing processExploratory researchMedical educationMedicinePsychologyComputer scienceSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To conduct an exploratory process evaluation of the Ontario Ministry of Health and Long-Term Care's (MOHLTC) New Graduate Nursing Initiative implemented by one home care agency. METHODS: Qualitative data were gathered online, stored electronically and then analyzed using an Affinity Diagram. RESULTS: Seven groupings of participants' comments were created: advertising and external information dissemination; orientation; internal dissemination; impact of the program; transition to the workforce; pay/benefits; and retention. Participants viewed many aspects of the program favourably but identified the following areas for improvement: comprehensibility of the Health Force Ontario website (advertising and external information); orientation of new graduates (orientation); and communication of information about the initiative to existing staff (internal dissemination). CONCLUSIONS: This exploratory study points to both strengths and weaknesses of the New Graduate Nursing Initiative. Further study of the implementation of this policy is recommended.

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)0.141
metaresearch head score (Gemma)0.090
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.747

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1410.090
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0060.004
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
GPT teacher head0.475
Teacher spread0.272 · 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

Citations11
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueHealthcare policySame topicNursing education and managementFrench-language works237,207