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Record W4200467912 · doi:10.5430/jnep.v12n5p13

The interactionism among nursing educators, preceptors and newly graduate nurses related to role transition: An exploratory study

2021· article· en· W4200467912 on OpenAlexvenueno aff
Badriya M. Al-Riyami, Arlene V. Pamplona, Amal J. Al-Hadabi

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleBachelorPsychologyNursingQualitative propertyNurse educationData collectionQualitative researchNurse educatorMedical educationMedicineSociologyComputer science

Abstract

fetched live from OpenAlex

Background and objective: The role transition remains a barrier in the ability of nurses to adjust from the role of student to professional nurse where the interactive mentoring attributes portrayed by nurse educators and clinical preceptors that may influence the role transition of new graduate nurses has not been thoroughly assessed. This study was conducted to explore the interactive mentoring attributes of nursing educators and clinical preceptors that influence new graduate nurses’ ability to successfully adjust from the role of student to that of professional nurse.Methods: Convergent parallel mixed method of research was used in this study, where both quantitative and qualitative data were simultaneously collected, analyzed, merged, compared and interpreted. The quantitative data came from purposively sampled first batch graduates of Bachelor of Science in Nursing at Oman College of Health Sciences A.Y. 2017-2018 (N = 33;n = 27) through survey using researcher-made survey questionnaire in 5-point-Likert scale format based on the five attributes of beginning theory of Faculty Attributes for Confidence, Equilibrium, and Success (FACES) by Sparacino L. (2016). The qualitative exploration focused on the most significant influential interactive mentoring attributes of nursing educators and preceptors solicited through interview. Quantitative data were statistically treated and interpreted using percentage, weighted mean, t test and Pearson’s correlation. Qualitative data representing each participant’s point of view were analyzed using open coding, transcribed, analyzed, compared, and categorized.Results: Quantitative findings revealed that the respondents strongly agreed on the influential effect of interactive mentoring attributes portrayed by their preceptors in terms of professionalism while they agreed with their nursing educators (composite mean: 4.1; 3.9) respectively. Respondents also agreed with the influential effect of knowledge and experiences as well as in terms of care and rigor attributes. However, the t test values and correlation analysis showed no significant relationship (p > .05) between the profile of the respondents in terms of GPA and department with the interactive mentoring attributes portrayed by nursing educators and preceptors during role transition. Qualitatively, caring, rigor and professionalism were the significant interactive mentoring attributes of nursing educators and preceptors most influenced newly graduate nurses on their ability to successfully adjust from the role of student to that of professional nurse.Conclusion and recommendation: The interactive mentoring attributes portrayed by nursing educators and preceptors have a positive influential effects in transition process, although the respondents’ profile were not significantly related nor the relationship between the role portrayed by nursing educators and preceptors to role transition. Therefore, it is recommended to use these findings in curriculum revision and in the modification of clinical orientation or training policy.

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)0.005
metaresearch head score (Gemma)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.458
Teacher spread0.393 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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