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Record W3201387240 · doi:10.1177/10497323211006383

“It’s a Bit of a Double-Edged Sword”: Motivation and Personal Impact of Bereaved Mothers’ Advocacy for Drug Policy Reform

2021· article· en· W3201387240 on OpenAlexafffundabout
Heather Morris, Elaine Hyshka, Petra Schulz, Emily Jenkins, Rebecca Haines‐Saah

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersUniversity of British ColumbiaUniversity of CalgaryKillam TrustsWomen and Children's Health Research InstituteMichael Smith Health Research BCSocial Sciences and Humanities Research Council of CanadaUniversity of Alberta
KeywordsEmpowermentQualitative researchGriefHarm reductionFeelingGeneral partnershipMental healthPsychological resilienceHarmPublic healthNursingMedicinePsychologyPublic relationsPsychiatryPolitical scienceSocial psychologySociologyLaw

Abstract

fetched live from OpenAlex

North America's overdose crisis is an urgent public health issue that has resulted in thousands of deaths. As the crisis began to take hold across Canada in 2016, bereaved parents, mainly mothers, emerged as vocal advocates for drug policy reform and harm reduction, using their stories to challenge the stigma of drug-related death. In 2017, we launched a qualitative research partnership with leading family organizations in Canada, conducting interviews with 43 mothers whose children had died from substance use, to understand their experiences of drug policy advocacy. Our findings showed that participants' motivations for engaging in advocacy were rooted in their experiences of grief, and that advocacy led to feelings of empowerment and connection to others. Our research suggests that advocacy can be cathartic and associated with healing from grief, but that "going public" in sharing a family story of substance use death can also have a considerable personal cost.

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)0.010
metaresearch head score (Gemma)0.003
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 score0.058
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.517
GPT teacher head0.672
Teacher spread0.155 · 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

Citations10
Published2021
Admission routes3
Has abstractyes

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