Spirometry for Improved Indigenous and Rural Respiratory Outcomes (SPIRO): A Learning Heath Systems Approach
Bibliographic record
Abstract
BACKGROUND: (one sentence)The purpose of the SPIRO Project is to increase access to and use of spirometry (the gold standard to diagnose and manage chronic obstructive pulmonary disease), with a specific focus on improving the health outcomes of people in remote, rural, and First Nations communities in the Northern Health (NH) and Vancouver Coastal Health (VCH) authorities in British Columbia (BC), Canada. APPROACH: The goal of SPIRO is to utilize an integrated, equity-focused, learning health system (LHS) approach to provide spirometry and associated respiratory care services to remote, rural, and Indigenous communities in BC, specifically in the health care jurisdictions of NH and VCH. Our focus is on improving health outcomes of people with COPD. The LHS approach has four key steps: ) Formation of Learning Community; 2) Practice to Data; 3) Data to Knowledge; and 4) Knowledge to Practice. The SPIRO Team supports the health authorities with each step, with specific attention to identifying questions for the Practice to Data step, and designing and evaluating implementation projects to improve access to and use of spirometry. Within each of these steps, we employ a mixed-methods approach, which includes embedded case studies, electronic medical record (EMR) synthesis, qualitative interviews, social network analysis, and administrative health data modelling. We follow the principles of community-based research and Indigenous health research including OCAP (Ownership, Control, Access, Possession) principles. RESULTS: Formation of Learning Community: We have formed a 40+ member team, comprised of patients, clinicians, researchers, First Nation health organizations, community members, industry partners, and health care decision-makers. Patients were identified from the VCH Legacy for Airway Health, through social media, and through clinicians. Patient partners have an approved Patient Engagement Plan, attend team meetings, and also have separate meetings to discuss process and results. Clinician members include family physicians, respirologists, and respiratory therapists. Researchers have expertise in COPD, LHS, implementation science, health equity, economic modelling, administrative health data analysis, team science, and First Nations research methods. Decision-makers represent NH and VCH at high levels of authority, and First Nations health delivery organizations. Practice to Data: Our team has confirmed the priority questions to complete the Practice to Data step. We completed an initial study which estimated the prevalence of COPD in remote, rural, and First Nations communities, based on administrative health records and random-sampled clinical studies. This enabled us to estimate the need for spirometry in these locations. We have also identified several care gaps, including: ) the lack of EMR data related to spirometry; 2) the fragmentation and health inequities of respiratory care services in the province, and 3) the barriers to accessing spirometry at the individual, practitioner and health system levels. IMPLICATIONS: By exploring the current state of spirometry services in remote, rural, and First Nations communities, SPIRO will generate evidence-based, integrated interventions to increase accessibility of testing, appropriate education during testing, continuous education for physicians in rural practice, and culturally safe care for First Nation communities. Our next step will be the Data to Knowledge Phase, where evidence-based and data-driven solutions will be identified and tested in real world settings in rural, remote, and First Nations communities. Our learnings will be applicable to the provinces and territories in Canada and globally where access to and use of spirometry are limited.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".