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Record W3111628715 · doi:10.1177/0030222820981238

Nursing Home Staff Members’ Experiences With and Beliefs About Unusual End-of-Life Phenomena

2020· article· en· W3111628715 on OpenAlexaff
Stephen Claxton‐Oldfield, Natalie Richard

Bibliographic record

VenueOMEGA - Journal of Death and Dying · 2020
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsMount Allison University
Fundersnot available
KeywordsTranspersonalFeelingNursingVisionMedicinePsychologySocial psychologyPsychotherapistSociology

Abstract

fetched live from OpenAlex

Twenty-two members of a nursing home took part in a study examining their experiences with and beliefs about unusual end-of-life phenomena (EOLP). Nearly all the staff members had witnessed and/or been told about residents holding on for someone to arrive or for a specific event to occur before dying (95% and 91%, respectively). Other commonly witnessed/reported EOLP included residents having sudden, unexpected moments of lucidity, sensing or feeling the presence of deceased residents, residents' dreaming about deceased relatives, friends or pets, and deathbed visions. More than three-quarters of the staff members regarded EOLP as transpersonal experiences, as comforting to dying residents and their family members, and as part of the dying process. Fourteen staff members described experiences they had had with EOLP in the nursing home. The most frequently described experiences involved the appearance of apparitions. Seventy-seven percent of the staff members expressed an interest in learning more about EOLP.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.316
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2020
Admission routes1
Has abstractyes

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