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Mentorship in Health Care Organizations: Managers’ Perspectives

2014· article· en· W2741710087 on OpenAlexaff
Noelle Rohatinsky, Linda Ferguson

Bibliographic record

VenueManagement Education An International Journal · 2014
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMentorshipHealth careNursingPsychologyMedicineBusinessPolitical scienceMedical education

Abstract

fetched live from OpenAlex

A priority for healthcare organizations has been health human resource planning. Employee mentoring is one approach that has been found to contribute to positive workplaces and thus facilitate recruitment and retention of staff. The purpose of this study was to develop a theory of nurse managers' perceptions of their roles in creating mentoring cultures within healthcare organizations. The objectives included: (a) exploring managers' perceptions of their role in creating a mentoring culture, (b) discovering the processes of creating a culture of mentoring, and (c) exploring the organizational features supporting and inhibiting this process of developing a mentoring culture. Glaserian grounded theory was the methodology used to conduct this research and twenty-seven nurse managers were interviewed. Managers believed all employees, from senior leadership to front line employees, needed to be committed to mentoring in order for mentorship to be successful within healthcare organizations and in order for mentoring cultures to be created. Participants identified several strategies that characterized employees’ commitment to mentoring. By implementing these findings, managers can assist to create quality workplaces by increasing job satisfaction and recruitment and retention of employees.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.639
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.369
Teacher spread0.354 · 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 designNot applicable
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

Citations2
Published2014
Admission routes1
Has abstractyes

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