Continuity of care for older adults in a Canadian long-term care setting: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Continuity of care has been shown to improve health outcomes and increase patient satisfaction. Goal-oriented care, a person-centered approach to care, has the potential to positively impact continuity of care. This study sought to examine how a goal-oriented approach impacts continuity of care in a long-term care setting. METHODS: Using a case study approach, we examined what aspects of goal-oriented care facilitate or inhibit continuity of care from the perspectives of administrators, care providers, and residents in a long-term care centre in Ontario, Canada. Data was collected through documentary evidence and semi-structured interviews. RESULTS: We analyzed six internal documents (e.g., strategic plan, client information package, staff presentations, evaluation framework, program logic model), and conducted 13 interviews. The findings indicated that the care provided through the goal-oriented approach program had elements that both facilitated and inhibited continuity of care. These factors are outlined according to the three types of continuity, including aspects of the program that influence informational, relational, and management continuity. CONCLUSIONS: Aspects of the goal-oriented care approach that facilitate continuity can be targeted when designing person-centered care approaches. More research is needed on goal-oriented care approaches that have been implemented in other long-term care settings to determine if the factors identified here as influencing continuity are confirmed.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".