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Record W4408227489 · doi:10.4103/cjrm.cjrm_76_24

Letter to the editor concerning ‘“I’m on the coast and I’m on methadone:” A qualitative study examining access to opioid agonist treatment in rural and coastal British Columbia’

2025· letter· en· W4408227489 on OpenAlexvenueaboutno aff
Saurabh RamBihariLal Shrivastava

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2025
Typeletter
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMethadoneAgonistOpioidMedicineAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Dear Editor, It is with great interest that we read the recently published original article entitled ‘“I’m on the coast and I’m on methadone:” A qualitative study examining access to opioid agonist treatment in rural and coastal British Columbia in the Canadian Journal of Rural Medicine.1 The authors have addressed an important topic, and we extend our appreciation to authors to identify the contextual factors that can influence access to opioid agonist treatment in rural and coastal settings.1 In the Methods section, the authors mentioned the word trustworthiness of results, which is the backbone of any qualitative study; nevertheless, it would have been really insightful how authors ensured credibility, dependability, confirmability and transferability in this study.2 Triangulation has been acknowledged as an important aspect of data collection, and it can be accomplished in the number of ways, namely data triangulation, interviewer triangulation and methodological triangulation.3 No such form of triangulation has been done, and thus it is not easy to believe about the credibility of results. Further, no mention has been done about the audit trail, reflexivity of the investigators, inter-rater reliability, data saturation, etc., all of which are integral pillars in the conduct of an effective qualitative study.2,4 I was happy to note that each interview lasted 45–60 min as it would have given an adequate time for the investigators to understand the topic in depth from participants’ perspective. However, the endpoint of all interviews has not been mentioned – whether the interviewers waited for data saturation or till all research questions were asked?3 We are happy that the authors acknowledged the study limitations, as it is an important part of data reporting. However, the article would have got more weightage, if the authors had given attention to different aspects of trustworthiness in this community-based qualitative study. Financial support and sponsorship: Nil. Conflicts of interest: There are no conflicts of interest.

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)0.013
metaresearch head score (Gemma)0.114
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 score0.966
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.114
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0060.006
Open science0.0040.003
Research integrity0.0190.029
Insufficient payload (model declined to judge)0.0080.003

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 head0.038
GPT teacher head0.326
Teacher spread0.287 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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