Barriers and Facilitators to Prescribed Safer Supply Prescribing Decisions at Carrier Sekani Family Services
Bibliographic record
Abstract
The toxic drug crisis in Canada kills an average of 21 people per day, with British Columbia reporting an average of six deaths daily in 2024. Most deaths are linked to illicit fentanyl. In 2020, British Columbia introduced prescribed safer supply (PSS) as a pharmaceutical alternative to the toxic drug supply. While early research supports PSS in reducing overdose deaths, prescribing rates remain low. Carrier Sekani Family Services is a branch society of the Carrier Tribal Council that provides First Nations-governed healthcare to 11 First Nations communities in Northern British Columbia. These communities have been severely affected by the toxic drug crisis prompting Carrier Sekani Family Services leadership to consider the implementation of PSS in their Member Nations. The objective of this research is to explore barriers and facilitators to PSS prescribing decisions at Carrier Sekani Family Services.For this qualitative description study, eight semi-structured interviews were conducted with healthcare providers at Carrier Sekani Family Services. The study was informed by the COM-B Framework. Thematic analysis was used to identify key barriers and facilitators to PSS prescribing decisions. Participants described factors influencing PSS prescribing decisions through four themes. The first theme, Navigating Social and Structural influences, captured structural constraints in a Northern, rural and remote setting and local opposition to PSS. The second theme, Complexities in Decision-Making and Knowledge Sharing, explored decision-making strategies of participants. The third theme, Moral Dilemmas in Care, reflects ethical challenges, emotional distress, and the impact of the toxic drug crisis on participants’ motivation. The final theme, Transformative Encounters, highlights alignment of PSS with participants’ professional and ethical values. Findings highlight the influence of structural, social, and personal factors on PSS prescribing decisions among healthcare providers in a First Nations-governed, rural, and remote healthcare setting. Addressing barriers such as limited health services and local opposition to PSS while strengthening facilitators like collaborative team dynamics may strengthen PSS prescribing at Carrier Sekani Family Services.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".