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Record W1819832495 · doi:10.1111/1467-9566.12344

Pathways linking drug use and labour market trajectories: the role of catastrophic events

2015· article· en· W1819832495 on OpenAlexafffundabout
Lindsey Richardson, Will Small, Thomas Kerr

Bibliographic record

VenueSociology of Health & Illness · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsSimon Fraser UniversityAIDS VancouverUniversity of British Columbia
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchNational Institutes of HealthMichael Smith Health Research BCPierre Elliott Trudeau Foundation
KeywordsDrugLabour economicsEconomicsBusinessMedicinePharmacology

Abstract

fetched live from OpenAlex

People affected by substance use disorders often experience sub-optimal employment outcomes. The role of drug use in processes that produce and entrench labour market precarity among people who inject drugs (PWID) have not, however, been fully described. We recruited 22 PWID from ongoing prospective cohort studies in Vancouver, Canada, with whom we conducted semi-structured retrospective interviews and then employed a thematic analysis that drew on concepts from life course theory to explore the mechanisms and pathways linking drug use and labour market trajectories. The participants' narratives identified processes corresponding to causation, whereby suboptimal employment outcomes led to harmful drug use; direct selection, where impairment, health complications or drug-seeking activities selected individuals out of employment; and indirect selection, where external factors, such as catastrophic events, marked the initiation or intensification of substance use concurrent with sudden changes in capacities for employment. Catastrophic events linking negative transitions in both drug use and labour market trajectories were of primary importance, demarcating critical initiation and transitional events in individual risk trajectories. These results challenge conventional assumptions about the primacy of drug use in determining employment outcomes among PWID and suggest the importance of multidimensional support to mitigate the initiation, accumulation and entrenchment of labour market and drug-related disadvantage.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.054
GPT teacher head0.335
Teacher spread0.281 · 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 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

Citations24
Published2015
Admission routes3
Has abstractyes

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