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Record W2084688056 · doi:10.5539/gjhs.v6n6p273

Coping Strategies Used by Iranian Nurses to Deal With Burnout: A Qualitative Research

2014· article· en· W2084688056 on OpenAlexvenueno aff
Mohammad Mehdi Salaree, Armin Zareiyan, Abbas Ebadi

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsnot available
FundersBaqiyatallah University of Medical Sciences
KeywordsNonprobability samplingBurnoutCoping (psychology)NursingQualitative researchContent analysisPsychological interventionPsychologyHealth carePerceptionIslamNursing Interventions ClassificationMedicineClinical psychologySociologyPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Although numerous studies have reported about coping strategies among health care worker throughout the world, but no research-based data are available on the perception of coping strategy among Clinical nurses in the Islamic Republic of Iran. OBJECTIVE: The aim of the present study was to describe and explore the experiences of Iranian nurses about their coping strategies. METHODS: In this study we used a qualitative research approach to explore how Iranian nurses perceive and resolve their burnout at work. Twelve nurses were selected by purposive sampling and in-depth semi structured interviews were conducted. All interviews were tape recorded, transcribed verbatim and then analyzed by means of the conventional qualitative content analysis method. RESULTS: The 5 main themes that evolved from content analysis included "religious responsibility", "approximation to God", "spiritual reward", "Holiness of the job" and "spiritual journey" emerged as the most important among these. CONCLUSIONS: The results of this study emphasized that religious or spiritual beliefs give purpose and meaning to nursing interventions, help them tolerate the problems at work, and make nursing care pleasurable. Therefore, although burnout is an important issue in nursing, attending to this dimension of their job is essential and healthcare authorities should pay a special attention to it.

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)0.007
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.093
GPT teacher head0.517
Teacher spread0.424 · 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 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

Citations19
Published2014
Admission routes1
Has abstractyes

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