Exploring subjective quality-of-life indicators in long-term care facilities: a mixed-methods research protocol
Bibliographic record
Abstract
INTRODUCTION: Improving quality of life has become a priority in the long-term care (LTC) sector internationally. With development and implementation guidance, standardised quality-of-life monitoring tools based on valid, self-report surveys could be used more effectively to benefit LTC residents, families and organisations. This research will explore the potential for subjective quality-of-life indicators in the interRAI Self-Reported Quality of Life Survey for Long-Term Care Facilities (QoL-LTCF). METHODS AND ANALYSIS: Guided by the Medical Research Council Framework, this research will entail a (1) modified Delphi study, (2) feasibility study and (3) realist synthesis. In study 1, we will evaluate the importance of statements and scales in the QoL-LTCF by administering Delphi surveys and focus groups to purposively recruited resident and family advisors, researchers, and LTC clinicians, staff, and leadership from international quality improvement organisations. In study 2, we will critically examine the feasibility and implications of risk-adjusting subjective quality-of-life indicators. Specifically, we will collect expert stakeholder perspectives with interviews and apply a risk-adjustment methodology to QoL-LTCF data. In study 3, we will iteratively review and synthesise literature, and consult with expert stakeholders to explore the implementation of quality-of-life indicators. ETHICS AND DISSEMINATION: This study has received approval through a University of Waterloo Research Ethics Board and the Social and Societal Ethics Committee of KU Leuven. We will disseminate our findings in conferences, journal article publications and presentations for a variety of stakeholders.
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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 | 0.183 | 0.099 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".