The use of technology at home: what patient manuals say and sell vs. what patients face and fear
Bibliographic record
Abstract
Over the past 15 years, the use of specialised medical equipment by patients at home has increased in most industrialised countries. Adopting a conceptual framework that brings together two research perspectives, i.e. the sociology of technology and the sociology of illness, this paper empirically examines why and how patients use health technology at home and in the broader social world. Our study compares and contrasts the use of four interventions: antibiotic intravenous therapy, parenteral nutrition, peritoneal dialysis and oxygen therapy. We conducted interviews with patients (n = 16) and caregivers (n = 6), and made direct observations of home visits by nurses (n = 16). The content and structure of patient manuals distributed by major manufacturers and hospitals were analysed (n = 26). The aim of our study was to determine how technology was supposed to be used versus how it was actually used. This study shows that patients are deeply ambivalent about the benefits and drawbacks of technology, and that these advantages and disadvantages are shaped by the various places in which the technology is used. While technology can be pivotal in making patients autonomous and able to participate in the social world, it also imposes heavy restrictions that are intimately interwoven with the nature of the particular disease and with the patient's personal life trajectory.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".