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Record W2884921035 · doi:10.1080/02682621.2018.1493646

Lived experiences of singing in a community hospice bereavement support music therapy group

2018· article· en· W2884921035 on OpenAlexafffund
Laurel Young, Adrienne Pringle

Bibliographic record

VenueBereavement Care · 2018
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsJoseph Brant HospitalConcordia University
FundersConcordia University
KeywordsSingingMusic therapyPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Although singing is an inherent part of grieving in many cultures, relatively little research has been conducted on how singing is experienced by adults in bereavement support group contexts. The purpose of this study was to examine the singing experiences of seven female adults who participated in a postloss bereavement support music therapy group that took place in a community hospice. Individual interviews were conducted with all participants, who also had the option of submitting written feedback after each group singing session. This feedback, along with interview transcripts, was analysed using Interpretative Phenomenological Analysis (IPA). This resulted in seven narrative summary interpretations that represent explicit and implicit aspects of each individual’s lived experience of singing in this context. Cross case analysis revealed themes organised under five categories, supported with participant quotes. Potential implications for research and practice are presented.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0070.006
Scholarly communication0.0020.001
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.356
Teacher spread0.295 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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