The Sustainment and Sustainability of Quality Improvement Initiatives for the Health Care of Older Adults
Bibliographic record
Abstract
Quality Improvement (QI) is increasingly viewed as the vehicle of choice by health care organizations seeking to respond to the changing health care needs of an aging population while making efficient use of limited resources. However, evidence suggests that 40% to 60% of QI initiatives fail to achieve lasting improvements. Despite the potential waste this statistic represents, we have a limited understanding of factors which promote a QI intervention’s continued use in practice (sustainment) and the maintenance of its benefits (sustainability). Moreover, existing research has largely focused on large academic institutions in urban centres. As such, recommendations derived from these studies may not be appropriate for less-resourced, rural, or remote settings, where adaptations—planned changes to the intervention—may be needed to ensure its survival. The impact of such adaptations on QI sustainment or sustainability has not been studied. This thesis draws from a scoping review and subsequent artificial neural network analysis of empirical studies of the sustainment and sustainability of QI interventions for the health care of older adults to identify contextual, intervention and implementation factors which predict sustainment and sustainability. This model significantly predicted sustainability, but not sustainment. Omission of organization type, intervention target, or reported adaptations resulted in a significant loss of predictive power. To further investigate this result, I conducted a comparative case study to examine how the dynamic interaction between adaptations and organizational context impacts sustainment and sustainability. By comparing 3-year trajectories for elder care QI interventions implemented through participation in a quality improvement collaborative in a 375-bed academic hospital and a 56-bed remote hospital 2500 km away from its nearest referral centre, I found evidence that ongoing adaptations which are responsive to changes in organizational or environmental context (e.g. patient needs, government and local health authority supports) promote sustainment. In addition, adaptations that promote sustainment and are consistent with the core functions of a QI intervention contribute to the sustainability of QI. To close, I offer a new framework informed by organizational learning theory which will help contextualize adaptations’ impact on the people, tasks and/or tools of an intervention and its long-term sustainment and sustainability.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.024 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".