MétaCan
Menu
← Back to cohort
Record W4391345570 · doi:10.4324/9781032657455-7

Drug policy and welfare systems as context for drug-related death bereavement

2024· book-chapter· en· W4391345570 on OpenAlexaboutno aff
Svanaug Fjær, Kari Dyregrov

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsDrugContext (archaeology)WelfarePolitical sciencePharmacologyMedicineLawHistory

Abstract

fetched live from OpenAlex

The experience of being bereaved after a drug-related death is formed by cultural and social contexts, as well as the welfare systems and drug policy in different countries. The research (from the END-project) shows that drug-death bereaved are latecomers on the political scene both in drug policy debates and in the more general debate on welfare policies and services to people bereaved after unnatural or unexpected deaths. Respondents in the END-project clearly express an experience of being discounted in policymaking, research and when it comes to service provision. To what extent do we find bereaved after drug-related death as a stakeholder group in political debates over drug policy reform, harm reduction strategies or service provision internationally? We have chosen Norway, Portugal, the United Kingdom, and Canada as contexts for the analysis of structural dimensions and stakeholders in the construction of the welfare response to the phenomenon of bereavement after drug-related death.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.020
Scholarly communication0.0090.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.332
Teacher spread0.303 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicGrief, Bereavement, and Mental Health→French-language works237,207→