Reconsidering the role of place in health and welfare services: lessons from the COVID-19 pandemic in the United States and Canada
Bibliographic record
Abstract
Places-the meaningful locations of daily life-have been central to the wellbeing of humans since they first formed social groups, providing a stable base for individuals, families, and communities. In the United States and Canada, as elsewhere, place also plays a foundational role in the provision of critical social and health services and resources. Yet the globally destabilizing events of the COVID-19 pandemic have dramatically challenged the concept, experience, and meaning of place. Place-centered public health measures such as lockdowns and stay-at-home orders have disrupted and transformed homes, neighborhoods, workplaces, and schools. These measures stressed families and communities, particularly among marginalized groups, and made the delivery of vital resources and services more difficult. At the same time, the pandemic has stimulated a range of creative and resilient responses. Building from an overview of these effects and drawing conceptually on theories of people-place relationships, this paper argues for critical attention to reconsidering and re-envisioning prevailing assumptions about place-centric policies, services, and practices. Such reappraisal is vital to ensuring that, going forward, scholars, policymakers, and practitioners can effectively design and deliver services capable of maintaining social connections, safety, and wellbeing in contexts of uncertainty, inequality, and flux.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.041 | 0.042 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".