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Record W4394989122 · doi:10.1080/0886571x.2024.2341778

Residential Youth Workers’ Perceptions of Grief and Loss in Residential Care

2024· article· en· W4394989122 on OpenAlexaff
Jacqueline Tams, Marcus Gottlieb, Jamie Piercy

Bibliographic record

VenueResidential Treatment for Children & Youth · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsResidential careGriefPerceptionPsychologyNursingEnvironmental healthMedicinePsychiatry

Abstract

fetched live from OpenAlex

Youth in residential care typically experience significant losses during their childhood, which often result in grief. The presence of supportive adults who acknowledge these losses has been found to be a significant protective factor for youth in residential care. Residential youth workers consistently interact with the children they support; thus, they have a unique opportunity to address children’s losses and associated grief. This project uses a mixed methods design to explore how residential youth workers conceptualize and account for grief and loss. Specifically, we explore service providers’ perceptions of how loss affects children’s behavior. Three narrative vignettes describing a hypothetical child’s behavior were used to elicit these opinions. Residential youth workers (n = 101) were recruited from British Columbia to complete a brief questionnaire that assessed their perceptions of behaviors they encounter and their experiences supporting grieving youth. A one-way ANOVA revealed no statistically significant group differences; however, a thematic analysis revealed that while grief and loss are often accounted for, participants feel under-resourced to provide grief-informed care to a population experiencing complex losses. The findings suggest that clinical and policy changes are necessary to better support residential youth workers and the youth they care for.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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