Design and introduction of a quality of life assessment and practice support system: perspectives from palliative care settings
Bibliographic record
Abstract
BACKGROUND: Quality of life (QOL) assessment instruments, including patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs), are increasingly promoted as a means of enabling clinicians to enhance person-centered care. However, integration of these instruments into palliative care clinical practice has been inconsistent. This study focused on the design of an electronic Quality of Life and Practice Support System (QPSS) prototype and its initial use in palliative inpatient and home care settings. Our objectives were to ascertain desired features of a QPSS prototype and the experiences of clinicians, patients, and family caregivers in regard to the initial introduction of a QPSS in palliative care, interpreting them in context. METHODS: We applied an integrated knowledge translation approach in two stages by engaging a total of 71 clinicians, 18 patients, and 17 family caregivers in palliative inpatient and home care settings. Data for Stage I were collected via 12 focus groups with clinicians to ascertain desirable features of a QPSS. Stage II involved 5 focus groups and 24 interviews with clinicians and 35 interviews with patients or family caregivers during initial implementation of a QPSS. The focus groups and interviews were recorded, transcribed, and analyzed using the qualitative methodology of interpretive description. RESULTS: Desirable features focused on hardware (lightweight, durable, and easy to disinfect), software (simple, user-friendly interface, multi-linguistic, integration with e-health systems), and choice of assessment instruments that would facilitate a holistic assessment. Although patient and family caregiver participants were predominantly enthusiastic, clinicians expressed a mixture of enthusiasm, receptivity, and concern regarding the use of a QPSS. The analyses revealed important contextual considerations, including: (a) logistical, technical, and aesthetic considerations regarding the QPSS as a technology, (b) diversity in knowledge, skills, and attitudes of clinicians, patients, and family caregivers regarding the integration of electronic QOL assessments in care, and (c) the need to understand organizational context and priorities in using QOL assessment data. CONCLUSION: The process of designing and integrating a QPSS in palliative care for patients with life-limiting conditions and their family caregivers is complex and requires extensive consultation with clinicians, administrators, patients, and family caregivers to inform successful implementation.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".