Alexander First Nation Members’ Views of Their Relationships With Community Pharmacists: A Qualitative Study Protocol
Bibliographic record
Abstract
Background: Indigenous Peoples face inequities in health and social determinants of health due to colonization. Pharmacists must create relationships with Indigenous Peoples because the patient care process depends on it and because relationality is essential to Indigenous Peoples. Pharmacists are well-positioned to provide care due to their accessibility and medication knowledge. Despite the need for a focus on decreasing health inequities, little research within pharmacy in Canada has focused on Indigenous perspectives. This research explores Alexander First Nation (AFN) members’ views of their relationships with community pharmacists in Alberta, Canada, regarding trust, expectations of pharmacists, and willingness to discuss traditional medicines. Methods: General principles of Indigenous research and Ethical Space informed the study design. Community-based participatory research (CBPR) was used, and the research was collaboratively designed with the Alexander Research Committee (ARC). Conversation methodology incorporates a long history of oral tradition with the Indigenous research principles of respect, relevance, and reciprocity. Inclusion criteria were AFN members living in Alexander or outside Alexander who had interacted with a pharmacist as an adult. Qualitative data were collected through one-on-one semi-structured interviews as conversations. Inductive thematic analysis was used to analyze the data. Rigor was ensured by hiring an AFN research assistant, member checking, thematic saturation, an audit trail, and reflexivity. Discussion: An equitable partnership with the ARC was developed using CBPR, which supported Indigenous ways of knowing and doing to be incorporated into the research design and data interpretation. Consequently, the study results will provide information about building relationships with pharmacists that can be delivered back to AFN in a way that respects local First Nation perspectives and is meaningful to community members. Our results will also enable us to provide pharmacists with information about creating mutually beneficial relationships, from a First Nation perspective, necessary for positive Indigenous health outcomes.
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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 | 0.054 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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.
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".