Evaluation of case management in tuberculosis control: a three-year effort to improve case management practices in New York City.
Bibliographic record
Abstract
OBJECTIVE: To describe a 3-year effort to identify factors associated with lapses in case management (CM) and to improve CM practices by New York City Bureau of Tuberculosis Control (BTBC) staff. DESIGN: Evaluation of the CM of TB cases reported in the second quarter of 2003 and comparison of results with the findings of a similar review conducted in 2002. Implementation of strategies to target areas in need of improvement and identification of interventions that contribute to improved work practices. RESULTS: From 2002 to 2003, significant improvements were found in some CM indicators, such as patient education about the importance of directly observed therapy (32% vs. 74%), importance of monthly follow-up (24% vs. 51%) and potential for development of drug resistance (36% vs. 61%). Informing patients about the availability of services provided by the BTBC also improved (16% vs. 59%); however, timeliness and documentation of CM activities and implementation of supervisory activities remained poor. Supervisors largely attributed this lack of improvement to poor documentation of work actually performed. CONCLUSIONS: These evaluations identified lapses in CM practices and program supervision. The findings were used to adjust protocols, target interventions, and focus education and training to improve work practices.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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, 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".