SP35 A Qualitative Examination of Factors that Influence Women'S Qol as they Live with Heart Disease
Bibliographic record
Abstract
Purpose: The purpose of this study was aimed at understanding women's experiences and perceptions regarding women health related quality of life in the context of living with heart disease. Method: Sampling was purposive and continued until data saturation. The women participated in semi-structured interviews, conducted in-person, which were audio-recorded and usually completed in less than one hour. The interviews were transcribed for analysis. Conventional content analysis was used to analyze the data. Results: Results indicated that heart disease influenced the participants QOL. This occurred through elements the investigators labeled “dealing with one more thing”; “surrendering roles and pleasures”; “managing the health system”; “understanding heart disease”; and “resolving to be strong”. Branching out from immediate family, social networks, including those developed through cardiac rehabilitation programs, played a significant role in women's descriptions of what contributed to having good QOL. These social networks also proved useful in helping the women manage the health system and understand their heart disease. Bringing friends/daughters along with them for their medical appointments assisted the women in ensuring that they understood what was being discussed at appointments and processes involved in managing their symptoms. Conversely, the women who lacked social networks reflected that the inability to manage their health contributed to their quality of life or lack thereof.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".