Improving access to primary healthcare for vulnerable populations in Australia and Canada: protocol for a mixed-method evaluation of six complex interventions
Bibliographic record
Abstract
INTRODUCTION: Access to primary healthcare (PHC) has a fundamental influence on health outcomes, particularly for members of vulnerable populations. Innovative Models Promoting Access-to-Care Transformation (IMPACT) is a 5-year research programme built on community-academic partnerships. IMPACT aims to design, implement and evaluate organisational innovations to improve access to appropriate PHC for vulnerable populations. Six Local Innovation Partnerships (LIPs) in three Australian states (New South Wales, Victoria and South Australia) and three Canadian provinces (Ontario, Quebec and Alberta) used a common approach to implement six different interventions. This paper describes the protocol to evaluate the processes, outcomes and scalability of these organisational innovations. METHODS AND ANALYSIS: The evaluation will use a convergent mixed-methods design involving longitudinal (pre and post) analysis of the six interventions. Study participants include vulnerable populations, PHC practices, their clinicians and administrative staff, service providers in other health or social service organisations, intervention staff and members of the LIP teams. Data were collected prior to and 3-6 months after the interventions and included interviews with members of the LIPs, organisational process data, document analysis and tools collecting the cost of components of the intervention. Assessment of impacts on individuals and organisations will rely on surveys and semistructured interviews (and, in some settings, direct observation) of participating patients, providers and PHC practices. ETHICS AND DISSEMINATION: The IMPACT research programme received initial ethics approval from St Mary's Hospital (Montreal) SMHC #13-30. The interventions received a range of other ethics approvals across the six jurisdictions. Dissemination of the findings should generate a deeper understanding of the ways in which system-level organisational innovations can improve access to PHC for vulnerable populations and new knowledge concerning improvements in PHC delivery in health service utilisation.
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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 | 0.128 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".