Primary care-based interventions to address patients' unmet economic needs
Bibliographic record
Abstract
Poverty is acknowledged as the largest single social determinant of health in many high-income countries. Research into income interventions in primary care settings to address the health impact of poverty is a nascent and evolving field, with many gaps in knowledge. This thesis sets out to fill three related knowledge gaps in three separate papers. The first is a scoping review of the literature, which examines existing interventions currently in use in high-income countries. This review provides a unique overview of income interventions across different primary care settings, gleaned from over 200 papers, focusing on interventions targeting economic needs, and investigating interventions in the primary care setting across the whole spectrum, from screening patients, and collecting and managing the data generated in the process, to referring patients to external services, and directly intervening to address patients’ needs. The second is a case study of an income security health promotion service in a family practice in Toronto, Ontario, Canada. The study is the first to gather perspectives of key informants involved in this service, and to understand its origins, context and functioning. The study explores the external forces and contextual factors that have shaped the origin and development of the service, and offers important insights into how to create and sustain such a programme in other primary care settings. The third paper looks at an environment with extremely high rates of poverty–Hong Kong–where there are no such interventions in place. Through interviews with family physicians, the study explores the multiple barriers to primary care responsiveness to poverty, as well as potential facilitators and avenues for change. In doing so, the paper offers pointers for the introduction of such interventions not only in Hong Kong, but also in other high-income settings with high levels of inequality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".