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Record W2753081102 · doi:10.1177/1049732317729139

The Emotional Labor of Personal Grief in Palliative Care: Balancing Caring and Professional Identities

2017· article· en· W2753081102 on OpenAlexaff
Laura Funk, Sheryl Peters, Kerstin Roger

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGriefThematic analysisPsychologyParticipant observationHealth careEmotional laborDisenfranchised griefCommodificationNursingPalliative careContext (archaeology)NormativeSocial psychologyNegotiationQualitative researchPsychotherapistMedicineSociology

Abstract

fetched live from OpenAlex

The paid provision of care for dying persons and their families blends commodified emotion work and attachments to two often-conflicting role identities: the caring person and the professional. We explore how health care employees interpret personal grief related to patient death, drawing on interviews with 12 health care aides and 13 nurses. Data were analyzed collaboratively using an interpretively embedded thematic coding approach and constant comparison. Participant accounts of preventing, postponing, suppressing, and coping with grief revealed implicit meanings about the nature of grief and the appropriateness of grief display. Employees often struggled to find the time and space to deal with grief, and faced normative constraints on grief expression at work. Findings illustrate the complex ways health care employees negotiate and maintain both caring and professional identities in the context of cultural and material constraints. Implications of emotional labor for discourse and practice in health care settings are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.345
GPT teacher head0.615
Teacher spread0.269 · 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

Citations91
Published2017
Admission routes1
Has abstractyes

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