MétaCan
Menu
Back to cohort

Being a Preceptor Is Stressful!

2002· article· en· W1974417089 on OpenAlexaff
Olive Yonge, Harvey Krahn, Lorraine Trojan, Doreen Reid, Mary Haase

Bibliographic record

VenueJournal for Nurses in Staff Development · 2002
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsGovernment of AlbertaUniversity of Alberta
Fundersnot available
KeywordsOverworkWorkloadPreceptorPsychologyNurse educatorStress (linguistics)NursingMedical educationMedicineNurse educationComputer science

Abstract

fetched live from OpenAlex

Results of a mail survey of 295 preceptors indicated preceptoring nursing students can be a stressful experience, with overwork identified as the main source of stress. Overwork resulted from unsuitability of students for the clinical area, lack of time, and insufficient feedback and guidance. The findings suggest that both students and preceptors require proper readiness assessment and preceptorship preparation. Preceptorship stress needs to be acknowledged; it can be addressed through workload adjustments and by providing feedback and support from nurse educators, peers, and managers.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.028
GPT teacher head0.321
Teacher spread0.293 · 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 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

Citations96
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueJournal for Nurses in Staff DevelopmentSame topicNursing education and managementFrench-language works237,207