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Record W2347115442 · doi:10.5430/jha.v5n4p49

The role of working environment in nurses’ career advancement from nursing mangers’ perspectives: A qualitative study

2016· article· en· W2347115442 on OpenAlexvenueno aff
Mohammad Reza Sheikhi, Masoud Fallahi‐Khoshknab, Farahnaz Mohammadi Shahboulaghi, Fatemeh Oskouie

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWorking environmentNursingShahidQualitative researchContext (archaeology)Content analysisMedicinePsychologySociologyPolitical scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Background: Nurses’ career advancement is a dynamic and unique concept which is explained in the context of working environment.Objective: This study aimed to explore the role of working environment in nurses’ career advancement from Nursing Mangers’ Perspectives.Methods: This qualitative study was conducted using content analysis method. Eighteen nursing managers from hospitals affiliated to Qazvin, Tehran, Iran and Shahid Beheshti Medical Sciences Universities participated in the study. A purposive sample of nursing mangers with rich experiences and maximum variations were selected and continued to reaching data saturation. The data were analyzed using content analysis method.Results: Participants believed that working environment have two major roles in nurses’ career advancement including motivating and restricting roles. According to nursing mangers, motivating working environment had facilitating role, while restricting working environment had blocking role in Iranian nurses’ career advancement.Conclusions: It seems that recognizing characteristics of working environment could assist nurses and nursing managers to develop conditions of working environment facilitating career advancement for nurses and decrease restrictive factors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.027
GPT teacher head0.365
Teacher spread0.338 · 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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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