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Record W4416439169 · doi:10.12927/cjnl.2025.27718

Supporting Nurse Practitioners Through Virtual and Hybrid Mentorship: Insights From Program Design and Delivery in Nova Scotia

2025· article· en· W4416439169 on OpenAlexaffvenueabout
Melanie Dunlop, Breanna Lloy, Sylvie Laprise, Nancy Cashen, Tricia S. Lane, Jennifer MacDougall, J. Perrin, Sohani Welcher

Bibliographic record

VenueNursing leadership · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsRegistered Nurses' Association of OntarioIzaak Walton Killam Health CentreNova Scotia Health Authority
Fundersnot available
KeywordsMentorshipNova scotiaWorkloadNurse practitionersDoctor of Nursing PracticeQuality (philosophy)Program evaluationAdvanced practice nursingAdaptation (eye)

Abstract

fetched live from OpenAlex

Newly graduated nurse practitioners (NPs) often face challenges transitioning into practice due to increased responsibility, limited support and unclear role expectations. This quality improvement study examined the implementation of a mentorship program for new NPs in Nova Scotia, supported by the Nursing Innovation Fund and developed in collaboration with Nova Scotia Health, the IWK Health Center, and the Department of Health and Wellness. Ten NPs engaged in virtual or hybrid mentoring relationships. Findings highlighted six months as a critical period for role identity, with ongoing workload and support challenges noted at 12 months. Results suggest mentorship must be flexible and tailored to evolving needs.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.718
Threshold uncertainty score0.561

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.185
GPT teacher head0.437
Teacher spread0.252 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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