mhealth technologies and doctor-patient relationships in the context of consumer culture in Winnipeg, Canada
Bibliographic record
Abstract
The purpose of this study was to examine how the use of mHealth technologies by lay individuals influenced their relationships with their doctors in the context of consumer cultures. Ten individuals living in Winnipeg, Canada who used varying mHealth technologies ranging from fitness devices, thermometers and cardiovascular monitoring devices for personal health monitoring were interviewed. Using reflexive sociological interviewing, the participants were engaged in conversations on health, their use of their devices, and their relations with their doctors. The interviews were conducted via Zoom Video conferencing between September to November 2020 and audio recorded using the audio recording function on Zoom. The interviews were transcribed verbatim and coded using the NVIVO software. Thematic analysis was used to analyse the data. The central themes that emerged from the interviews are the monitored life; consumerism in health; mHealth, health decisions and doctors; interpersonal doctor-patient relationships. The themes were interpreted using existing literature, Foucault’s concept of biopower and Lupton’s digital cyborg assemblage. Participants could be identified as consumer-patients who used mHealth technologies to gain knowledge on their bodies and health. The knowledge gained is specific and unique to participants’ health needs which they use to practice health and self-care individually or with assistance from their doctors. With this, mHealth technologies influence doctor-patient relationships such that patients partner with doctors in diagnosing and treatment. However, depending on the social location, patients may either be passive patients or consumer patients during medical encounters. These findings contribute to the existing literature on the use of mHealth technologies and consumerism in doctor-patient relationships.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".