Appropriating and asserting power on inflammatory arthritis teams: A social network perspective
Bibliographic record
Abstract
BACKGROUND: Therapeutic interventions for people with inflammatory arthritis (IA) increasingly involve multidisciplinary teams and strive to foster patient-centred care and shared decision making. Participation in health-care decisions requires patients to assert themselves and negotiate power in encounters with clinicians; however, clinical contexts often afford less authority for patients than clinicians. This disadvantage may inhibit patients' involvement in their own health care. OBJECTIVE: To identify communication attributes, IA patients use to influence and negotiate their treatment with members of their health-care network. METHOD: A qualitative social network approach was used to analyse data from a larger study that investigated IA patients' overall experiences of multidisciplinary care. Fourteen patients with IA attended individual semi-structured interviews. Researchers used thematic analysis to identify patterns of assertiveness and influence in the data. RESULTS: Participants experienced loss of identity, control and agency in addition to the physical symptoms of IA. However, they had a sense of personal responsibility for managing their health care. Perceptions of health-care team support enhanced patients' influence in treatment negotiations. Notably, there appeared to be an underlying tension between being empowered or disempowered. DISCUSSION AND CONCLUSIONS: The findings have significant implications for treatment decision communication approaches to IA care. A social network perspective may provide a pathway for clinicians to better understand the complexities of communication with their patients. This approach may reduce unequal power dynamics that occur within clinician/patient interactions and afford people with IA agency, control and affirmation of identity within their health-care network.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".