Patient empowerment and involvement in telemedicine
Bibliographic record
Abstract
Objective: Telemedicine is a rapidly expanding area, and this article discusses the implications of patient empowerment and user involvement in relation to frail patients. Our aim is to critique the mechanical way telemedicine is being implemented in the health sector.Methods: We present the basic ideas of empowerment and user involvement behind telemedicine, exemplifying them with a case of user resistance to telemedicine. Four logics of empowerment are employed to identify the underlying rationale in specific cases of telemedicine. The case comes from a large evaluation of new welfare technology products. The data consist mainly of written documents and an interview.Results: Telemedicine is often considered a way to increase empowerment and user involvement in healthcare. The majority of the geriatric patients in the described case refused to engage in telemedicine, preferring instead to be hospitalized. The case appeared to be driven primarily by a professional logic of empowerment. User involvement and empowerment are discussed in terms of their demands and implication for users, such as 1) intrusion on the private sphere, i.e., the home and 2) the question of the responsibility for treatment and 3) the expectation, that the capabilities and resources of patients and relatives may increase.Conclusions: Although telemedicine is acknowledged as relevant, a mechanical approach too often hampers empowerment for the patient. Some patient groups may not feel safe using telemedicine, in which case user involvement and empowerment are not possible.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".