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Record W2969780398 · doi:10.12968/ijpn.2019.25.8.398

Is the bereavement grief intensity of survivors linked with their perception of death quality?

2019· article· en· W2969780398 on OpenAlexaffabout
Donna M. Wilson, Joachim Cohen, Cecilia Eliason, Luc Deliëns, Rod MacLeod, Jessica Hewitt, Dirk Houttekier

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2019
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGriefPsychologyPalliative carePerceptionDisenfranchised griefComplicated griefTraumatic griefClinical psychologyQuality (philosophy)MedicineNursingPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Some people experience exceptionally severe bereavement grief, and this level of post-death grief could potentially be the result of a low quality dying process. AIMS: A pilot study was conducted to determine if a relationship exists between perceived death quality and bereavement grief intensity. METHODS: A questionnaire was developed and posted online for data on bereavement grief intensity, perceived death quality, and decedent and bereaved person characteristics. Data from 151 Canadian volunteers were analysed using bi-variate and multiple linear regression tests. FINDINGS: Half had high levels of grief, and over half rated the death as more bad than good. Perceived death quality and post-death grief intensity were close to being negatively correlated. CONCLUSION: These findings indicate research is needed to explore possible connections between bereavement grief and the survivor's perceptions of whether a good or bad death took place. In the meantime, it is important for palliative care nurses to think of the quality of the dying process as being potentially very impactful on the people who will be left to grieve that death.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.474

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 head0.077
GPT teacher head0.413
Teacher spread0.336 · 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 designObservational
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

Citations12
Published2019
Admission routes2
Has abstractyes

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