Policy versus practice: a community-based qualitative study of the realities of pharmacy services in Nunavut, Canada
Bibliographic record
Abstract
OBJECTIVES: Nunavut is an Arctic territory in Canada subject to many social, economic and health disparities in comparison to the rest of the nation. The territory is affected by health care provision challenges caused by small, geographically isolated communities where staffing shortages and weather related access barriers are common concerns. In addition to national universal healthcare, the majority of the inhabitants of Nunavut (~85 %) are Inuit beneficiaries of no-charge pharmaceuticals provided through federal and/or territorial budgetary allocations. This research examines how existing pharmaceutical administration and distribution policies and practices in Nunavut impact patient care. METHODS: This grounded theory research includes document analysis and semi-structured interviews conducted in 2013/14 with patients, health care providers, administrators and policy makers in several communities in Nunavut. Thirty five informants in total participated in the study. Interviews were audiotaped, transcribed and analyzed with qualitative data analysis software for internal consistency and emerging themes. RESULTS: Four distinct themes emerge from the research that have the potential to impact patient care and which may provide direction for future policy development: 1) tensions between national versus territorial financial responsibilities influence health provider decisions that may affect patient care, 2) significant human resources are utilized in Community Health Centres to perform distribution duties associated with retail pharmacy medications, 3) large quantities of unclaimed prescription medications are suggestive of significant financial losses, suboptimal patient care and low adherence rates, and 4) the absence of a clear policy and oversight for some controlled substances, such as narcotics, leaves communities at risk for potential illegal procurement or abuse. CONCLUSIONS: Addressing these issues in future policy development may result in system-wide economic benefits, improved patient care and adherence, and reduced risk to communities. The interview informants who participated in this research are best positioned to identify issues in need of attention and will benefit the most from policy development to address their concerns.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".