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Record W3108214827 · doi:10.1111/add.15325

Commentary on Richardson <i>et al</i> . : Strategies to mitigate payment‐coincident drug‐related harms are urgently needed

2020· letter· en· W3108214827 on OpenAlexaboutno aff
Alexandria Macmadu, Josiah D. Rich

Bibliographic record

VenueAddiction · 2020
Typeletter
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersNational Institute of General Medical Sciences
KeywordsRecessionHarmPaymentReceiptUnemploymentBusinessHarm reductionEconomicsMedicineEconomic growthPublic healthPolitical scienceFinanceLaw

Abstract

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Drug-related harms that coincide with synchronous income assistance payments are prevalent among people who use drugs. Given the global economic recessions precipitated by the COVID-19 pandemic, and the resulting unprecedented reliance on synchronous income assistance payments, strategies to mitigate these harms—while also disseminating necessary income assistance—are urgently needed. Richardson et al. [1] found that among people who use drugs (PWUD) and receive monthly income assistance, the majority (78%) of participants reported experiencing drug-related harm in the days surrounding receipt of income assistance. This research extends prior literature examining drug-related harm coinciding with synchronized income assistance payments [2-4], the so-called ‘check effect’, by quantifying the prevalence of these harms and identifying socio-economic and drug-related correlates. The high prevalence of payment-coincident drug-related harms identified by Richardson et al. underscores an urgent need to explore strategies that mitigate these harms while also disseminating necessary income assistance, particularly in light of international responses to the COVID-19 pandemic. COVID-19 has precipitated global economic recessions and unprecedented reliance upon synchronous income assistance payments. According to the World Bank, the global economy is expected to shrink by 5.2% in 2020 alone, representing the deepest global recession since the end of World War II [5]. In response, many countries have implemented direct support programs that provide synchronous income assistance payments to those who lost income due to COVID-19. Over the next year, an estimated 890 000 Canadians are expected to receive the Canada Recovery Benefit [6]. As of October, 10.2 million people in the United States received regular Pandemic Unemployment Assistance payments [7], and many other countries have expanded unemployment insurance programs for the foreseeable future. Depression, anxiety and substance use have also increased precipitously during the COVID-19 pandemic [8], and an unprecedented number of PWUD are receiving synchronous income assistance payments. The most common payment-coincident drug-related harms identified by Richardson et al. [1] were intensified drug and alcohol use. Many other payment-coincident harms were also identified, including non-fatal overdose, discontinuing substance use disorder treatment and being unable to access a health or social service or supervised injection facility due to increased demand. To mitigate risk of overdose and reduce barriers to critical services, supervised injection facilities and other harm reduction service providers (e.g. those who distribute naloxone, injecting equipment and fentanyl test strips) should consider expanding their operational capacities in the days surrounding synchronized income assistance payments when possible through increased staffing or expanded service hours. Other health and social service providers could also consider such expansions. While social distancing guidelines, shutdowns and fiscal challenges have complicated the delivery of harm reduction services [9], many programs have adopted less restrictive, needs-based models to increase service distribution [10]. Similar strategies could also be adapted to meet payment-coincident increases in demand. Expanded monetary support for harm reduction supplies and services will also be needed to ensure access to key services [11]. Alongside COVID-19 public health messaging that is tailored towards marginalized PWUD [11], harm reduction campaigns that address intensive alcohol and drug use during days surrounding payment distribution should also be developed and piloted. Directing resources and services to individuals at greatest risk of payment-coincident harms, such as those experiencing a high degree of socio-economic and structural marginalization or engaging in high-intensity drug use, will also be essential to mitigate harms. Richardson et al. [1] also found that residency in the Downtown Eastside, a neighborhood of Vancouver that is characterized by economic disadvantage, was associated with payment-coincident drug-related harm. As the authors note, prior research investigating the ‘check effect’ phenomenon at the neighborhood level in Rhode Island did not identify an association between the proportion of residents receiving monthly income assistance and excess payment-coincident overdose mortality, although it was associated with the proportion of residents living in unaffordable housing [12]. Correspondingly, in developing a robust response to reducing payment-coincident drug-related harms, other structural stressors that co-occur with synchronous income assistance payments—such as rent/mortgage payments and elevated evictions risks—should also be considered. COVID-19 has exacerbated a pre-existing global housing crisis which is already affecting millions, particularly PWUD [13]. Strategies that curtail housing-related stressors and reduce housing instability, such as expanded availability of temporary emergency housing [14], extending moratoriums on evictions [15], deferrals of mortgage and rental payments [16], rent stabilization and reduction measures [17] and an overall expansion of affordable housing, are also critically needed. As Richardson et al. [1] highlight, payment-coincident drug-related harms are prevalent among PWUD, and individuals who are marginalized or engaging in high-intensity drug use experience elevated risk. In the era of COVID-19, when reliance upon synchronous income assistance payments has never been greater, strategies to mitigate payment-coincident drug-related harms are urgently needed. None.

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)0.012
metaresearch head score (Gemma)0.080
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.070
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.080
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0020.002
Science and technology studies0.0070.007
Scholarly communication0.0070.009
Open science0.0110.004
Research integrity0.0700.067
Insufficient payload (model declined to judge)0.0220.020

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 head0.034
GPT teacher head0.344
Teacher spread0.310 · 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
GenreCommentary

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

Citations3
Published2020
Admission routes1
Has abstractyes

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