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Record W2966411141 · doi:10.22605/rrh5347

'We have to drive everywhere': rural nurses and their precepted students

2019· article· en· W2966411141 on OpenAlexaffabout
Olive Yonge, Deirdre Jackman, Florence Luhanga, Florence Myrick, Tracy Oosterbroek, Vicki Foley

Bibliographic record

VenueRural and Remote Health · 2019
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of Prince Edward IslandUniversity of ReginaUniversity of LethbridgeUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsSnowball samplingPhotovoiceNursingQualitative researchParticipant observationPsychologyRural areaMedicineMedical educationSociology

Abstract

fetched live from OpenAlex

INTRODUCTION: Travel safety culture is a vital aspect of nursing in rural western Canada, where long distances and severe weather are commonplace. However, this culture is poorly understood owing to the absence of official policy, and the tendency of rural nurses to take travel risks and burdens in stride, rather than advocating for change. Travel risks and burdens include extreme weather events such as tornadoes and blizzards; unmarked routes and hazards; distance, time and expense; and driver fatigue. In such rural settings, the safety and health of visitors, novices and students are of particular concern. The researchers sought to elicit the tacit knowledge of rural registered nurses, and their students undertaking rural nursing preceptorships, pertaining to rural travel issues and best practices for safety and wellbeing. METHODS: Through purposive and snowball sampling, the researchers recruited seven senior nursing students and five nurse preceptors. Seven rural acute and community care sites, between 42 km and 416 km distant from the students' primary place of study, were covered by the study. Photovoice, a participant action modality, was employed to collect photographic and qualitative interview data from participants over 10 weeks, between February and April 2016. The data were analyzed thematically, in collaboration with participants, who in turn validated the results. A digital storytelling initiative was attempted, to further involve participants in dissemination of findings, but only one participant took part in this phase of the project. RESULTS: The central finding of the study was that nursing students learn to accept and manage limitations - and to recognize and capitalize on opportunities - when undertaking rural preceptorships. With regard to road safety, the students were found to be particularly vulnerable to long distances, hazardous conditions, fuel and cellular data expenses, and fatigue. These issues were compounded by the students' reluctance to speak up, or to miss shifts, when they felt unsafe or unwell. Their preceptors role modeled autonomy and community ethos as the foundations of a frontline, extemporaneous road safety culture. This entailed personal safety measures borne from rural experience and background, familiarity with the countryside, and community connectedness with other healthcare sites in place of any official public alert system. The preceptors furthermore benefited from strong union protection for occupational health and safety concerns, but students being taught in rural settings had no such advantage. CONCLUSIONS: Nursing students should have the same occupational health and safety protections as their rural preceptors, especially the right to refuse travel, without penalty, in unsafe circumstances. Better travel subsidies and road safety measures during rural preceptorship may help increase the likelihood of students considering a rural career path. Furthermore, the frontline, community-based road safety experience of rural nurses is an untapped source of information for educators and policymakers. Such information will become more and more vital as a diminishing number of rural nurses are called upon to care for an aging client base.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.343
Teacher spread0.323 · 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 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

Citations6
Published2019
Admission routes2
Has abstractyes

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