Exploring Strategies for Developing Enabling Environments for People with Chronic Heart Disease: An Ethnographic Study Protocol
Bibliographic record
Abstract
The impact of chronic diseases on people's daily lives and the exponential number of people affected is a major public health issue. The consequences on individuals and their families is significant, particularly in terms of quality of life. In the literature, this phenomenon is well described in terms of care policy and cost. Although there is a link between a supportive environment and empowerment, there is little literature describing a supportive environment and the daily lives of people living with cardiovascular disease. The objectives of this study are to identify the strategies people use to develop an enabling environment. It will be a qualitative ethnographic study that will address both human behavior and the notion of culture in a broad sense. In the context of this study, an orientation towards critical ethnography will be considered for its particular interest in vulnerable people and in the power relations that may exist in the socio-cultural system. Data will be collected directly in people's homes through observations and interviews with 10 people with cardiovascular disease. For each person, the data collection will take place over three days and will represent approximately 210 h of observation. This protocol was registered in the Research Register on 30 June 2021 and its number is 6933. This study will explore strategies for developing an enabling environment for people living with heart disease and eventually provide recommendations for nursing practices in terms of support.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".