Primary care-based interventions to address patients' unmet economic needs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 it