How place matters: unpacking technology and power in health and social care
Bibliographic record
Abstract
The devolution of care into nontraditional community-based settings has led to a proliferation of sites for health and social care. Despite recent (re)formulations of 'evidence-based' approaches that stress the importance of optimizing interventions to best practice by taking into account the uniqueness of place, there is relatively little guidance in the literature and few attempts to systematically 'unpack' key dimensions of settings most relevant to policy, practice and research. In this paper, we explore how place matters for health and social care. In effect, we propose making place the lens through which to view practice, and not simply an interesting sideline focus. We focus specifically on (a) the emplacement of power relations in health and social care in and across settings; and (b) the pervasive (and often unrecognised) influence of technology on and in place (both 'mundane' and more visible 'high' technologies) as arguably among the most significant and pervasive (and often overlooked) dimensions of place pertinent to health and social care in both traditional (institutional) and nontraditional (community) settings. Drawing on diverse disciplinary literatures, we seek to make visible certain issues and bodies of work that health professionals may not be aware of, and which often remain inaccessible to practitioners and applied researchers on account of their density, complexity, and specialised terminology. In particular, drawing on the rich tradition of cultural studies, we advance the culture of place as a rubric for understanding the complex interrelationship between power, technology, culture, and place. Several fruitful avenues for place-sensitive research of health and social care practice (and its effects) are suggested.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.015 | 0.177 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".