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Record W4399915690 · doi:10.1080/07481187.2024.2369889

Developing a storybook package for bereaved siblings: A pilot study of the effectiveness for enhancing the perceived knowledge and confidence of health and social care professionals in Hong Kong

2024· article· en· W4399915690 on OpenAlexaff
Wallace Chi Ho Chan, Clare Tsz Kiu Yu, Grace Leung, Molin Kwok Yin Lin, Miranda Mei Mui Leung, Denis Ka Shaw Kwok, Jody Ka-Wing Wu

Bibliographic record

VenueDeath Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThematic analysisPsychologyHealth professionalsQualitative researchHealth careRandomized controlled trialNursingMedical educationApplied psychologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

A pilot randomized controlled trial was conducted to examine the effectiveness of a storybook package for enhancing the perceived knowledge and confidence of health and social care professionals in working with bereaved child siblings and their parents before and after the loss. Open-ended questions were asked to collect feedback, and thematic analyses were conducted to generate the themes. Quantitative findings provided preliminary but not strong evidence of its effectiveness, but qualitative findings showed that participants perceived their knowledge about supporting bereaved siblings and their parents was enhanced and considered the storybook package a useful tool for facilitating their practice. Participants also reflected on how real and specific the stories in the storybook should be. This study is the first step in developing an evidence-based practice tool for health and social care professionals. Future studies are required to further examine its effectiveness for practice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.406
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.140
GPT teacher head0.460
Teacher spread0.319 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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