A Mixed-Methods Exploration to Develop and Test the Alberta Cardiac Surgery Patient Experiences Survey
Bibliographic record
Abstract
With an increased focus on patient-centred care (PCC), many organizations conduct routine surveys as part of their core business. Many surveys that are used in the hospital setting have been designed to capture the experiences of a wide variety of patients. Thus, they do not ask condition-specific questions which may be important to patients. This mixed-methods thesis focused on examining and improving upon the measurement of patient experience among those who have undergone cardiac surgery. The first part of this thesis used existing survey data linked with administrative records to examine the comprehensive experience of Albertans who underwent cardiac surgery over a four-year period. Part two was a qualitative project, where interviews were conducted to better understand the aspects of care deemed important to patients after cardiac surgery. In the final portion, learnings from our prior quantitative and qualitative work were integrated to develop and test a new condition-specific survey; the Alberta Cardiac Surgery Patient Experiences Survey (ACSPES). In project one, patients reported very positive experiences. However, they did reveal potential areas for improvement. These included communication about potential side effects of new medications, night noisiness of the hospital environment, and cleanliness of their room/bathroom. In project two, participants highlighted five key themes important in their care - overall experience, communication, the physical hospital environment, care needs and ongoing management, and person-centred care. These findings aligned with those from project one, but also served to generate additional items which could be asked in a future survey. Project three demonstrated promising results pertaining to the content validity, test-retest reliability, and acceptability of the newly created ACSPES. This thesis work has increased our understanding and learnings about the experiences of those who have undergone cardiac surgery across Alberta. It has also resulted in the creation of the ACSPES; a tool which may be used to better capture the unique experiences of cardiac surgery patients. Data from the ACSPES may be used to measure PCC in this cohort and may be integrated with administrative and other patient-reported data for future learnings. Additional psychometric testing of the ACSPES is required.
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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 | 0.086 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".