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Record W2791024867 · doi:10.1515/ijnes-2017-0077

Mentoring as a Knowledge Translation Intervention for Implementing Nursing Practice Guidelines: A Qualitative Study

2018· article· en· W2791024867 on OpenAlexaffabout
Ghadah Abdullah, Kathryn A. Smith Higuchi, Jenny Ploeg, Dawn Stacey

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2018
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsOttawa HospitalMcMaster UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedical educationQualitative researchKnowledge translationNursingPsychologyIntervention (counseling)MedicineComputer scienceSociologyKnowledge management

Abstract

fetched live from OpenAlex

An interpretive descriptive qualitative study was conducted to explore the characteristics and outcomes of mentoring used for implementing nursing practice guidelines. We interviewed six mentees, eight mentors, and four program leaders who were involved in the Registered Nurses' Association of Ontario fellowship program in Ontario, Canada. Inductive content analysis was used and study rigor was verified using triangulation of findings and member checking. Mentors were described as accessible, dedicated, and having expertise; mentees were described as enthusiastic, self-directed, and having mixed levels of expertise. The mentoring process included building relationships, developing learning plans, and using teaching and learning activities guided by learning plans to support development of mentees. Mentoring was described as positively impacting mentoring relationships, mentees, mentors, and organizations. A central feature of this fellowship program was the learning plan used to identify mentees' needs, guide mentoring activities, and monitor measureable outcomes.

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 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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.405
GPT teacher head0.642
Teacher spread0.237 · 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 teacher head, 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

Citations7
Published2018
Admission routes2
Has abstractyes

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