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Record W4200620155 · doi:10.1177/00302228211053062

Development and Psychometric Evaluation of the Concerns of Grieving Caregivers Scale (COGCS) with Two Clinical Samples

2021· article· en· W4200620155 on OpenAlexaff
Samantha O’Leary, Christopher Quinn‐Nilas, Victoria Pileggi, Ceilidh Eaton Russell

Bibliographic record

VenueOMEGA - Journal of Death and Dying · 2021
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsMcMaster UniversityUniversity of WaterlooOntario Ministry of LabourUniversity of GuelphOntario Centre of Excellence for Child and Youth Mental Health
Fundersnot available
KeywordsGriefScale (ratio)PsychologyExploratory factor analysisInternal consistencyClinical psychologyConsistency (knowledge bases)Family caregiversClinical PracticeNursingPsychotherapistPsychometricsMedicine

Abstract

fetched live from OpenAlex

The Concerns of Grieving Caregivers Scale (COGCS) is the first of its kind to explore caregivers' concerns about their own parenting, as well as their relationships with, and specific behaviours of their bereaved child(ren). Using exploratory factor analysis, we evaluate grieving parents' and caregivers' concerns using data collected across clinical populations from two community organizations supporting grieving families (i.e., a children's grief centre and a community hospice). Two identified factors were established: Concerns about Caregiving and Concerns about the Child. The COGCS demonstrates good internal consistency and criterion validity in its application with two distinct clinical samples. The use of this scale could be of value to clinicians supporting bereaved caregivers and their families as they can integrate concern-specific resources into their practice to better support their clients' presenting concerns.

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)0.013
metaresearch head score (Gemma)0.026
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: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.188
GPT teacher head0.432
Teacher spread0.245 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOMEGA - Journal of Death and DyingSame topicGrief, Bereavement, and Mental HealthFrench-language works237,207