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Record W7055632510

Developing self-efficacy as nurse preceptors: a qualitative descriptive study

2020· dissertation· en· W7055632510 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNucleofectionTSG101HyporeflexiaArticular cartilage damageFilter (signal processing)Paraphernalia
DOInot available

Abstract

fetched live from OpenAlex

Preceptors are expected to facilitate students’ learning and the transition of new graduate nurses in the clinical area but may not be fully prepared for the role. The literature abounds with evidence supportive of considerable preceptor development yet researchers have been slow to explore preceptors’ perceptions of their capabilities and how they influence their willingness and behaviors. This qualitative descriptive study explores nurse preceptors’ perceptions of their self-efficacy when precepting senior practicum students. Albert Bandura’s self-efficacy theory guided this study. Individual semi-structured interviews were conducted with eight participants. Qualitative content analysis was used to analyze the data. Findings included two major themes, experiencing self-efficacy as a nurse preceptor and developing self-efficacy as a nurse preceptor. Previous preceptor experience, years of clinical practice experience, practicum experience as students and workplace supports were identified as salient sources of preceptor self-efficacy. Findings provided insight into the current state of self-efficacy in the preceptor role and contributed to addressing a gap in the literature. Findings have implications for strategies for development of self-efficacy and provided insight into other potential resources for supporting nurse preceptors in the development of their knowledge and behavior. The findings can inform the groundwork for research on further consideration for the implementation of structured transition programs in Canada.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.290
Teacher spread0.267 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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