History and Measurement of Continuity of Care in Mental Health Services and Evidence of Its Role in Outcomes
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to provide a brief history of the concept of continuity of care, to update evidence of its association with patient outcomes, and to identify optimal characteristics of a continuity-of-care instrument. METHODS: Articles describing recent (1990 to 2002) empirical work on continuity of care were drawn from a broader set of 305 articles about continuity of care that were obtained from a systematic literature search. RESULTS AND DISCUSSION: The literature shows that ideas about continuity of care have changed in concert with general service delivery changes over the decades. Since 1997, only eight studies have used operationally defined measures either to describe continuity of care in mental health services or to examine the association of continuity of care with outcomes for adults with severe and persistent mental illness. Only three groups of researchers have published articles on development of continuity-of-care measures. CONCLUSIONS: There is little evidence that continuity of care results in better client outcomes, which may be primarily attributable to the underdevelopment of measures. Measurement of continuity of care must become more sophisticated before key questions about the association of continuity of care with outcomes can be examined and before the effectiveness of interventions designed to improve continuity of care can be rigorously evaluated.
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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 | 0.031 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".