Advocacy of Women Family Caregivers: Response to Nonsupportive Interactions With Professionals
Bibliographic record
Abstract
Support from health and social service professionals assists women in caring for a relative with a chronic condition. However, nonsupportive interactions coexist with supportive interactions and might have unanticipated consequences. The purpose of this ethnographic study was to examine advocacy as a proactive response to nonsupportive interactions with professionals among women family caregivers in four caregiving situations. Data generation included in-depth interviews with 34 women. Interviews were tape-recorded, transcribed verbatim, and analyzed using thematic and constant comparative analysis techniques. As a consequence of nonsupportive interactions women experienced negative feelings, a lack of trust, powerlessness, and challenges in their caregiving situations that were catalysts for advocacy involving assertively taking charge in a relationship with a health professional. As advocates women employed strategies of monitoring their relative's condition, educating themselves or others, negotiating or fighting for resources, or campaigning for change. There were stress and fatigue involved in becoming an advocate, but the women also described the experience as one of personal growth. This research provided insight into the role of nonsupportive interactions with professionals as a catalyst for the development of individual-level advocacy initiatives.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".