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Record W2147916151 · doi:10.5430/jnep.v5n2p17

An exploratory study of the perspectives of clinical preceptors about difficult student situations during clinical teaching of final year undergraduate nursing students

2014· article· en· W2147916151 on OpenAlexvenueno aff
Joshua Kanaabi Muliira, Dennis C. Fronda, Savithri Raman

Bibliographic record

VenueJournal of Nursing Education and Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsPreceptorQualitative researchPsychologyExploratory researchMedical educationPerceptionFocus groupNursingIncivilityPatient careMedicineSocial psychology

Abstract

fetched live from OpenAlex

The aim of this study was to explore the perceptions of clinical preceptors (CPs) about difficult student situations during clinical teaching and the strategies they use to deal with such situations in the clinical setting. The participants were nurses who perform the role of clinical preceptor for senior nursing students at a University Hospital. The study used a descriptive qualitative approach to collect data through focus group discussions (FGD). Transcribed FGD data were content analyzed and coded to generate categories of difficult student situations in the clinical setting. The findings show that the CPs perceptions about difficult student situations in the clinical setting fall under four major categories of slothfulness, obstinateness, attentiveness and selfishness. The CPs also identified some of the strategies they use to manage these difficult student situations. In conclusion, it seems that during clinical teaching CPs experience challenging difficult student situations some of which can be considered as incivility and have significant implications for clinical teaching or learning and patient care outcomes. There is need for further studies about difficult student situations and incivility in the clinical setting and the effect it has on clinical student teaching, learning outcomes, and patient safety.

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.122
GPT teacher head0.545
Teacher spread0.424 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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