“I Go On The Internet; I Always, You Know, Check To See What’s New”: Chronically Ill Women’s Use of Online Health Information to Shape and Inform Doctor-Patient Interactions in the Space of Care Provision
Bibliographic record
Abstract
Information about health, medications, and illness management programs is becoming increasingly available online. For women living with medically unexplained illnesses such as fibromyalgia syndrome (FMS), explorations in cyberspace and the information gathered from the Internet as a result can be used to enlighten, and sometimes misinform, them about the chronic illnesses with which they are living. In this paper, I use data generated from 55 in-depth interviews with women living with FMS in Ontario, Canada to illustrate the relational connections between cyberspace and the space of care provision. More specifically, I demonstrate the ways in which information gathered online is used to inform, and prepare for, doctor-patient interactions in the space of care provision as a form of engaging in “patient agency.” The paper provides a useful glimpse into what it is like to be a patient in the “information age.” The analysis also assists in addressing the question: what is it that patients do with the health information they gather online? I conclude by revisiting how accessing health information via the Internet has implications for women’s performances as patients and the (re)negotiation of traditional constructions of power in the doctor-patient relationship.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".