Corrigendum to “Identifying the impacts of the COVID-19 pandemic on service access for people who use drugs (PWUD): A national qualitative study” [Journal of Substance Abuse Treatment 129 (2021) 108374]
Bibliographic record
Abstract
The authors regret the omission of a vital acknowledgement section. The authors would like to thank everyone who contributed to this study's success. Specifically, we would like to thank the national CRISM team, including NPIs (Dr. Evan Wood; Dr. Cameron Wild; Dr. Julie Bruneau); Node managers (Denise Adams; Nirupa Goel; Aissata Sako); Node researchers (Jade Boyd; Elaine Hyshka; Jennifer Swansburg); PWLE advisory members and the service providers who supported us throughout the project, including with participant recruitment; The Quebec Node (Kristine Gagnon Lafond; Marianne Quenneville-Dumont) and Ontario Node (Rashmi Narkar; Jeanette Bowles) researchers who provided their invaluable time interviewing participants and assisting with data translation and coding; as well as the individual PWUD participants who shared their lived expertise with use, without which the study could not have been conducted. The authors would like to apologise for any inconvenience caused. Identifying the impacts of the COVID-19 pandemic on service access for people who use drugs (PWUD): A national qualitative studyJournal of Substance Abuse TreatmentVol. 129PreviewClosures and reductions in capacity of select health and social services in response to the COVID-19 pandemic may have placed people who use drugs (PWUD) at a disproportionately increased risk for experiencing harms, and resulted in critical treatment disruptions. We conducted the current national study among a cohort of PWUD to understand how COVID-19 has affected service access, including any significant impacts PWUD may have experienced. Results will contribute to the evidence base for informing future pandemic and public health policy planning for vulnerable populations. Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.078 | 0.023 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".