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Record W2892029318 · doi:10.1111/jonm.12649

Group mentorship programme for graduating nursing students to facilitate their transition: A pilot study

2018· article· en· W2892029318 on OpenAlexaff
Mélanie Lavoie‐Tremblay, Lia Sanzone, Gilbert Primeau, Geneviève L. Lavigne

Bibliographic record

VenueJournal of Nursing Management · 2018
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMcGill University
Fundersnot available
KeywordsMentorshipNursing managementNursingMedical educationMedicinePsychology

Abstract

fetched live from OpenAlex

AIM: The present study aims to describe and evaluate a group mentorship programme for graduating nursing students following the first pilot testing. BACKGROUND: A mentoring relationship has been found to be beneficial in easing the challenging transition from nursing student to nurse. However, very few mentoring programmes have been formally developed to pair students with clinical nurses before graduation. METHODS: A group mentoring programme for graduating nursing students was developed where clinical nurse mentors met with student mentees twice before graduation and once shortly after graduation to address relevant challenges. Mentees and mentors completed a survey after the last session. RESULTS: Eighteen mentees and 12 mentors completed the survey. Results suggest a high level of satisfaction with the programme from both mentees and mentors, as well as a positive impact on mentees' transition into the workplace and levels of stress and self-confidence. CONCLUSIONS: The pilot testing of the group mentorship programme is believed to have been successful. IMPLICATIONS FOR NURSING MANAGEMENT: This pilot project highlights the value to nursing leadership of group mentoring partnerships between academic and clinical settings, which can improve the integration of new nurses in the workplace and increase mentors' awareness of the needs of these nurses.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.168
GPT teacher head0.391
Teacher spread0.223 · 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.

Study designOther design
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

Citations21
Published2018
Admission routes1
Has abstractyes

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