Treatment adherence redefined: a critical analysis of technotherapeutics
Bibliographic record
Abstract
Treatment adherence issues in the context of chronic illnesses have become an important concern worldwide and a top priority in the field of health-care. The development of devices that will allow healthcare providers to track treatment adherence and monitor physiological parameters with exact precision raises important questions and concerns. The aim of this study is to interrogate the use of these new technological devices which allow for previously unavailable data to be recorded on an ongoing basis and transmitted via a tiny microchip inserted into the body. Drawing on the work of Michel Foucault, we analyze how this anatomo-political and bio-political instrument serves to discipline chronically ill individuals and govern the health of entire populations who suffer from chronic conditions. To support our analysis, this article comprises three sections. First, we provide an overview of treatment adherence and technotherapeutics. Then, we explain how technotherapeutics concern the government of bodies and conducts at the individual level and population level more generally. Lastly, we provide an example of how this analysis can be connected to routine nursing practice in the field of HIV.
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 | 0.056 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| 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 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".