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Record W3096726995 · doi:10.12968/ijpn.2020.26.7.332

Death education for children and young people in public schools

2020· article· en· W3096726995 on OpenAlexaff
Hannah Friesen, Jennifer Harrison, Melissa Peters, Donna Epp, Nancy McPherson

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2020
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsBrandon University
Fundersnot available
KeywordsDeath educationCoping (psychology)Palliative careNursingPublic educationMedicineDeath anxietyPsychologyAnxietyMedical educationPsychiatryPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Conversations about death are often associated with fear, anxiety, avoidance and misunderstandings. Many adults feel that these discussions are inappropriate and confusing for young people. In this project, two fourth-year nursing students partnered with a local palliative care team to examine death education for children. The nursing students focused on children's understandings of death and their coping abilities, the lack of appropriate discussions about death with children, and the implementation of death education in public schools. Three online death education resources were identified and evaluated for use in public schools. This project fueled preliminary local discussions and advocacy efforts in the provision of death education for children. In the future, death education will need to be incorporated into education plans at local schools, and could be done in collaboration with the local palliative care team.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.058
GPT teacher head0.415
Teacher spread0.357 · 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 designNot applicable
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

Citations44
Published2020
Admission routes1
Has abstractyes

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