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Record W2929070398 · doi:10.1111/tct.13019

Evaluation of unsatisfactory student performance

2019· article· en· W2929070398 on OpenAlexafffund
Maria Pratt

Bibliographic record

VenueThe Clinical Teacher · 2019
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMcMaster UniversityHamilton Health Sciences
FundersRegistered Nurses’ Foundation of Ontario
KeywordsMentorshipOnboardingOpenness to experiencePsychologyMedical educationTask (project management)WorkloadNursingMedicinePedagogySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The 'failure to fail' phenomenon has been reported in studies involving preceptors and students in nursing and in other practice professions, but it has yet to be the subject of exploration among clinical nursing instructors. Research has revealed that assigning a failing grade in a practice profession is not always a straightforward task; however, passing students who are incompetent in their nursing practice could have a deleterious impact on the quality and delivery of patient care. METHODS: The author interviewed eight clinical instructors who had failed unsatisfactory students to gain an in-depth understanding of each instructor's experience in evaluating these students. Gadamer's hermeneutic principles were used to interpret the interview texts and dialogue with the participants through the hermeneutic circle of understanding, use of the researcher's prior experience and openness to new understanding. FINDINGS: The experiences of the clinical instructors in this study suggest that they have struggled with their emotions, particularly when they were novices, during the multiple personal, professional and organisational challenges that they have encountered within their teaching roles and responsibilities. DISCUSSION: The difficulties and challenges highlighted in this study may offer insight for academic stakeholders in nursing schools on how to mitigate these issues through developing an onboarding orientation and mentorship programme that will support faculty member development in the evaluation of unsatisfactory student performance.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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)0.0010.000

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 head0.149
GPT teacher head0.465
Teacher spread0.316 · 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 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

Citations5
Published2019
Admission routes2
Has abstractyes

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