Studying Scale-Up and Spread as Social Practice: Theoretical Introduction and Empirical Case Study
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
BACKGROUND: Health and care technologies often succeed on a small scale but fail to achieve widespread use (scale-up) or become routine practice in other settings (spread). One reason for this is under-theorization of the process of scale-up and spread, for which a potentially fruitful theoretical approach is to consider the adoption and use of technologies as social practices. OBJECTIVE: This study aimed to use an in-depth case study of assisted living to explore the feasibility and usefulness of a social practice approach to explaining the scale-up of an assisted-living technology across a local system of health and social care. METHODS: This was an individual case study of the implementation of a Global Positioning System (GPS) "geo-fence" for a person living with dementia, nested in a much wider program of ethnographic research and organizational case study of technology implementation across health and social care (Studies in Co-creating Assisted Living Solutions [SCALS] in the United Kingdom). A layered sociological analysis included micro-level data on the index case, meso-level data on the organization, and macro-level data on the wider social, technological, economic, and political context. Data (interviews, ethnographic notes, and documents) were analyzed and synthesized using structuration theory. RESULTS: A social practice lens enabled the uptake of the GPS technology to be studied in the context of what human actors found salient, meaningful, ethical, legal, materially possible, and professionally or culturally appropriate in particular social situations. Data extracts were used to illustrate three exemplar findings. First, professional practice is (and probably always will be) oriented not to "implementing technologies" but to providing excellent, ethical care to sick and vulnerable individuals. Second, in order to "work," health and care technologies rely heavily on human relationships and situated knowledge. Third, such technologies do not just need to be adopted by individuals; they need to be incorporated into personal habits and collaborative routines (both lay and professional). CONCLUSIONS: Health and care technologies need to be embedded within sociotechnical networks and made to work through situated knowledge, personal habits, and collaborative routines. A technology that "works" for one individual in a particular set of circumstances is unlikely to work in the same way for another in a different set of circumstances. We recommend the further study of social practices and the application of co-design principles. However, our findings suggest that even if this occurs, the scale-up and spread of many health and care technologies will be neither rapid nor smooth.
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 | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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