Eliciting Patient Experiences About Their Care After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Experience surveys provide an opportunity for patients to give their feedback about health care processes and services. Unfortunately, the most current surveys have been designed as "one-size fits-all" tools, and thus, do not take into account items pertaining to specific clinical groups. The objective of this study was to gain a deeper understanding of the specific aspects of care deemed important to cardiac surgery patients. METHODS: Individual semistructured telephone interviews were conducted with a cohort of patients who had previously underwent cardiac surgery. Interviews were recorded and transcribed. Using a phenomenological approach, a thematic analysis was used to generate a list of themes and subthemes deemed important by participants. RESULTS: Eight interviews were conducted in July and August 2019. Participants included 7 men and 1 woman, ranging from 55 to 84 years of age. Five key themes emerged from the data: (1) overall experience; (2) communication; (3) the physical hospital environment; (4) care needs and ongoing management; and (5) person-centred care. Our interviews revealed that participants had many overwhelmingly positive experiences with care. Through reports of their own experiences, participants highlighted important areas that might be improved. CONCLUSIONS: Our results confirm and expand upon those highlighted in quantitative research by our group. Findings and knowledge derived from this study might be used to inform quality improvement activities. These might also play a key role in the development of a patient experience survey, specifically for those who undergo cardiac surgery; thus addressing a potential limitation of surveys currently in use.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".