4 Quality of life assessment in palliative care day services
Bibliographic record
Abstract
Introduction The outcome measure used to assess Quality of Life (QoL) can have a crucial effect on the interpretation of any findings in palliative care. Aims To compare the sensitivity to change of the Schedule for the Evaluation of Individual Quality of Life-Direct Weighing (SEIQol-DW), the McGill Quality of Life Questionnaire (MQOL) and the Short Form 36 (SF-36) among patients receiving Palliative Care Day Services (PCDS). Methods Patients were recruited from PCDS. The same questionnaires were administered at 2 time points, approximately 3 weeks apart. A measure of dependency (the Barthel Index) was used to determine whether patients remain stable/deteriorate over time. Analysis Standardised response mean (SRM) was used to determine the relative sensitivity of each instrument for those patients who had changed in their Barthel scores (Garratt et al 1994). Results Forty four patients (median 69 years, range 49–86) were recruited. Follow-up data was obtained from 25 (56.8%) patients. Of these 25 patients, 14 (56%) patients changed according to the Barthel Index with 7 (50%) patients defined as increased dependency. The responsiveness of the SEIQoL-DW was larger than those of the other questionnaires (SRM=0.73) for those patients who showed an increased dependency over time. The SF-36 Bodily Pain subscale demonstrated a larger responsiveness than the other questionnaires (SRM=0.82) for those patients who showed a decrease dependency over time. Conclusions The SEIQoL appears to be a good outcome measure for charting the progress of patients who are deteriorating over time. However none of the questionnaires were particularly good at demonstrating improvements over time. Reference 1. Garratt AM, Ruta DA, Abdalla MI, Russell IT. SF-36 health survey questionnaire: II. Responsiveness to changes in health status in four common clinical conditions. Quality in Health Care1994;3:186–192.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".