Qualitative Research Informing a Preference Study on Selecting Cannabis for Cancer Survivor Symptom Management: Design of a Discrete Choice Experiment
Bibliographic record
Abstract
INTRODUCTION: The legalization of recreational cannabis use can enable cancer survivors to manage aspects of their care with cannabinoids without medical authorization or stigmatization. However, the absence of medical guidance-from the scientific literature or the healthcare system-makes it difficult for survivors to reach informed decisions about their care. OBJECTIVE: This article outlines the qualitative research undertaken to design a discrete choice experiment (DCE) aimed at understanding Canadian cancer survivors' preferences for managing their cancer symptoms with cannabis in this complex socio-medical context. METHODS: In this study, we drew on previously published qualitative research (a literature review and interviews with cancer survivors) and the theory of planned behavior, holding weekly team meetings to review the qualitative data and identify initial attributes associated with medicinal cannabis consumption to inform the DCE design. The initial attributes were further assessed to determine whether they were sensitive to the Canadian context, modifiable to produce levels and trade-offs, and amenable to policy intervention, in order to form the DCE choice sets. The choice sets were tested via think-aloud exercises with members of the general population and included debriefing interviews. Think-aloud participants were recruited from patient groups and previous studies. RESULTS: Based on our review of the interview study, we identified the following attributes associated with selecting medicinal cannabis: effectiveness; chance of side effects; support from family, friends, and/or physicians; cost; and availability. Ability to perform everyday activities was added and monthly out-of-pocket cost was refined to render the DCE realistic to cancer survivors in the Canadian context. Revisions to the DCE instructions, terminology, and cost levels were made based on results from the think-aloud exercises (n = 10). CONCLUSIONS: This qualitative study outlines the preference evidence collected regarding Canadian cancer survivors' decisions to manage their symptoms with cannabis to inform a DCE quantitative survey. It contributes to transparent reporting of qualitative work in DCE development and to understanding cancer survivors' preferences regarding medicinal cannabis consumption under legalization.
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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 | 0.059 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".