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Record W2600057367 · doi:10.5430/jnep.v7n8p80

A concept analysis of “Reluctance to fail”

2017· article· en· W2600057367 on OpenAlexvenueno aff
Sarah A. Prichard, Peggy Ward‐Smith

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetic reluctancePsychologyProcess (computing)Intervention (counseling)Psychological interventionGraduation (instrument)ReputationPhenomenonApplied psychologyComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

Background: Academic success in programs of nursing requires successful completion of didactic and clinical activities. Failure, in didactic situations, is objectively determined. Clinical failure is determined subjectively, which may expose the competency and reputation of the clinical faculty. This scenario can result in a hesitancy, or a reluctance to fail a student. Graduation may occur in the presence of limited clinical competency resulting in new graduates who are not adequately prepared for professional nursing practice.Methods: Exploring the concept of reluctance to fail will provide a conceptual definition based on uses of the concept found in research studies. Walker and Avant (2011) describe an eight step concept analysis process which will be utilized to determine the defining attributes, antecedents, consequences and empirical referents of the concept of reluctance to fail.Results: The result of this concept analysis is a conceptual model depicting reluctance to fail as a circular phenomenon with various elements. Guided by the intervention needed to address the deficiency, these elements may be placed in one of three categories: education of faculty, role modeling, and peer support.Conclusions: Education of clinical faculty will diminish the unwillingness, and hesitancy elements. Role modeling activities will prevent fear as rationale for reluctance to fail. Peer support provides emotional support when guilt for assigning a failing grade occurs. Future research must be conducted to identify factors responsible for faculty reluctance to assign failing grades, as well as the effectiveness of these interventions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.185
GPT teacher head0.575
Teacher spread0.390 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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