A multi-stakeholder perspective on asthma care in Canada: findings from a mixed methods needs assessment in the treatment and management of asthma in adults
Bibliographic record
Abstract
BACKGROUND: Although several aspects of asthma care have been identified as being sub-optimal in Canada, such as patient education, practice guideline adoption, and access to care, there remains a need to determine the extent to which these gaps remain, so as to investigate their underlying causes, and potential solutions. METHODS: An ethics-approved mixed methods educational needs assessment was conducted in four Canadian provinces (Alberta, British Columbia, Ontario, and Quebec), combining a qualitative phase (45-min semi-structured interviews with community-based healthcare providers and key stakeholders) and a quantitative phase (15-min survey, healthcare providers only). RESULTS: A total of 234 participants were included in the study, 44 in semi-structured interviews and 190 in the online survey. Five clinical areas were reported to be suboptimal by multiple categories of participants, and specific causes were identified for each. These areas included: Integration of guidelines into clinical practice, use of spirometry, individualisation of asthma devices to patient needs, emphasis on patient adherence and self-management, and clarity regarding roles and responsibilities of different members of the asthma healthcare team. Common causes for gaps in all these areas included suboptimal knowledge amongst healthcare providers, differing perceptions on the importance of certain interventions, and inadequate communication between healthcare providers. CONCLUSIONS: This study provides a better understanding of the specific causes underlying common gaps and challenges in asthma care in Canada. This information can inform future continuing medical education, and help providers in community settings obtain access to adequate materials, resources, and training to support optimal care of adult patients with asthma.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".