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Record W2464480734

[Supporting preceptor skills development with a forum discussion].

2013· article· fr· W2464480734 on OpenAlexaff
Linda Bonnier, Johanne Goudreau, Johanne Déry

Bibliographic record

VenuePubMed · 2013
Typearticle
Languagefr
FieldHealth Professions
TopicNursing care and research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsPreceptorGeneral partnershipFeelingMedical educationPsychologyInstitutionNursingMedicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

The success of nursing students in clinical settings is significantly influenced by the nurse's preparation level of the preceptor's role. It is therefore essential to create and make available training activities that prepares and support the development of these skills in clinical settings. Among the significant benefits to participate in preceptorship training activities, the satisfaction of feeling better prepared and to share experiences with colleagues is well recognized. Thus, the online environment allows the creation of programs that encourage exchanges and discussions in addition to promote a social learning space. This article presents the implementation of an online training activity to support the development of preceptors skills in clinical settings with nursing students. The results of this clinical experience revealed several constraints to the involvement of nurses in this activity and enhanced the importance of an organizational partnership between the institution and the clinical environment to overcome these constraints.

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)0.023
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: none
Teacher disagreement score0.122
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1220.039

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 head0.033
GPT teacher head0.370
Teacher spread0.337 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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