Older Persons’ Transitions in Care (OPTIC): a study protocol
Bibliographic record
Abstract
BACKGROUND: Changes in health status, triggered by events such as infections, falls, and geriatric syndromes, are common among nursing home (NH) residents and necessitate transitions between NHs and Emergency Departments (EDs). During transitions, residents frequently experience care that is delayed, unnecessary, not evidence-based, potentially unsafe, and fragmented. Furthermore, a high proportion of residents and their family caregivers report substantial unmet needs during transitions. This study is part of a program of research whose overall aim is to improve quality of care for frail older adults who reside in NHs. The purpose of this study is to identify successful transitions from multiple perspectives and to identify organizational and individual factors related to transition success, in order to inform improvements in care for frail elderly NH residents during transitions to and from acute care. Specific objectives are to: 1. define successful and unsuccessful elements of transitions from multiple perspectives; 2. develop and test a practical tool to assess transition success; 3. assess transition processes in a discrete set of transfers in two study sites over a one year period; 4. assess the influence of organizational factors in key practice locations, e.g., NHs, emergency medical services (EMS), and EDs, on transition success; and 5. identify opportunities for evidence-informed management and quality improvement decisions related to the management of NH - ED transitions. METHODS/DESIGN: This is a mixed-methods observational study incorporating an integrated knowledge translation (IKT) approach. It uses data from multiple levels (facility, care unit, individual) and sources (healthcare providers, residents, health records, and administrative databases). DISCUSSION: Key to study success is operationalizing the IKT approach by using a partnership model in which the OPTIC governance structure provides for team decision-makers and researchers to participate equally in developing study goals, design, data collection, analysis and implications of findings. As preliminary and ongoing study findings are developed, their implications for practice and policy in study settings will be discussed by the research team and shared with study site administrators and staff. The study is designed to investigate the complexities of transitions and to enhance the potential for successful and sustained improvement of these transitions.
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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 | 0.048 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".