Meeting the Burden of Self-management: Qualitative Study Investigating the Empowering Behaviors of Patients and Informal Caregivers
Bibliographic record
Abstract
BACKGROUND: Patient empowerment is an important concept and a movement toward person-centered care of patients with chronic conditions. Nevertheless, to date, most research on empowered patients or informal caregivers has been conducted from a narrow clinical perspective. Such research has mainly focused on how health care professionals can empower patients to increase self-care or compliance with treatment. Research on empowered patient and informal caregiver needs and self-empowering activities is scarce. OBJECTIVE: We aimed to explore empowering behaviors from a patient and informal caregiver perspective in the context of self-management and to understand how health care can support such behaviors better. METHODS: We used an exploratory, qualitative study design. A total of 15 semistructured interviews and 6 focus group interviews were conducted with 48 patients and informal caregivers. We analyzed the interviews using thematic analysis and used a directed content analysis to analyze the focus group interviews. RESULTS: A total of 14 patterns of empowering behaviors were identified that were characterized by several exploratory and influencing activities performed by the participants. The participants expressed a desire to be more active in their care than what is expected and supported by health care professionals. The participants also desired better support for activities imposed on them by health care professionals. CONCLUSIONS: To enable a transformation of the health care system to better support self-empowering behaviors, there is a need to develop self-management approaches from a patient and informal caregiver perspective.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".