Review. Adherence to recommendations of community-based comprehensive geriatric assessment programmes
Bibliographic record
Abstract
BACKGROUND: non-adherence to the recommendations of short-term community-based consultative comprehensive geriatric assessment programmes is a threat to the effectiveness of these programmes. OBJECTIVE: to synthesize the literature on patient and physician adherence to recommendations of community-based comprehensive geriatric assessment programmes. METHOD: I identified papers cited by an English language literature search of MEDLINE, Health Star and CINAHL databases from January 1980 to November 1999. This search was supplemented with literature identified from the reference sections of these publications. RESULTS: patient adherence rates ranged from 46 to 76%, which approximates to the rates for the consulting physician adherence (49-79%). I identified many characteristics of patient, treatment, care provider and clinical setting which influenced adherence. Understanding these factors has led to the development of adherence-enhancing strategies. However, without systematic evaluations it is difficult to evaluate the relative effectiveness of these interventions. CONCLUSION: further research which targets more representative samples and uses validated assessment tools and multiple data collection methods is needed to expand our knowledge of patterns and predictors of adherence and to evaluate the relative effectiveness of adherence-enhancing intervention strategies.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".