MétaCan
Menu
Back to cohort
Record W2158901450 · doi:10.1155/2012/528580

Putting the (R) Ural in Preceptorship

2012· article· en· W2158901450 on OpenAlexaff
Deirdre Jackman, Florence Myrick, Olive Yonge

Bibliographic record

VenueNursing Research and Practice · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsAlberta Hospital Edmonton
Fundersnot available
KeywordsNursingMedicineCompetence (human resources)Context (archaeology)MandateRural healthRural managementSocializationRural areaMedical educationPsychologyRural development

Abstract

fetched live from OpenAlex

Rural nursing is recognized as a unique health care domain. Within that context, the preceptorship experience is purported to be an important approach to preparing safe and competent rural practitioners. Preceptorship is the one-to-one pairing of a nursing student with a professional nurse who assumes the mandate of teacher and role model in a designated clinical/contextual setting, in this case the rural setting. A research gap exists in the literature in which rural preceptorship is specifically explored. The purpose of this paper is to review preceptorship in relation to preparing nursing students specifically for the rural setting. Understanding how preceptorship as an educational model can prepare nursing students to transition to rural practice is an important endeavor. An authentic rural preceptorship may serve to influence the recruitment and retention needs for registered nurses in rural areas. A greater understanding of rural preceptorship serves to illustrate the appropriate support, socialization and contextual competence required to prepare nursing students for rural nursing practice. This paper's review may serve to highlight the research that currently exists related to rural preceptorship and where additional research can contribute to further understanding and development for authentic rural nursing preparation.

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)0.017
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.348
GPT teacher head0.642
Teacher spread0.294 · 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 designNot applicable
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

Citations5
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueNursing Research and PracticeSame topicGlobal Health Workforce IssuesFrench-language works237,207