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Record W2884955117 · doi:10.1188/18.cjon.398-406

Oncology Volunteers: The Effect of a Personal Cancer History on Compassion and Psychological Well-Being

2018· article· en· W2884955117 on OpenAlexaff
Alexandra Meyer, Chelsea Moran, Tanya R. Fitzpatrick, Jochen Ernst, Annett Körner

Bibliographic record

VenueClinical journal of oncology nursing · 2018
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCompassionOncologyCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The impact of support work on volunteers with a personal history of cancer has rarely been examined, despite the possibility that supporting distressed individuals may become a psychological burden for someone who has faced a life-threatening disease themselves. OBJECTIVES: The purpose of this study is to compare compassion, self-compassion, self-coldness, and psychological well-being of oncology volunteers to the general population and clinical samples. METHODS: Volunteers completed questionnaires on demographic and volunteer work-related characteristics, the Compassion Toward Others Scale, the Self-Compassion Scale, and the Psychological General Well-Being Index. FINDINGS: Overall, volunteers indicated higher levels of self-compassion and psychological well-being and lower levels of self-coldness than clinical and community samples. Peer volunteers were less satisfied with their volunteer work and reported worse general health and psychological well-being than volunteers without a cancer history.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.118
GPT teacher head0.507
Teacher spread0.389 · 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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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