Human resource implications of expanding latent tuberculosis patient care activities
Bibliographic record
Abstract
Introduction: The World Health Organization (WHO) declared increasing services for latent tuberculosis infection (LTBI) a priority to eliminate tuberculosis (TB) by 2035. Yet, there is little information about thehuman resource needs required to implement LTBI treatment scale-up. Our study aimed to estimate the change in healthcare workers (HCW) time spent on different patient care activities, following an intervention to strengthen LTBI services. Methods: We conducted a time and motion (TAM) study, observing HCW throughout a typical workday before and after the intervention (Evaluation and Strengthening phases, respectively) at 24 health facilities in five countries. The precise time spent on pre-specified categories of work activities was recorded. Time spent on direct patient care was subcategorized as relating to one of three conditions: LTBI, active or suspected TB, and non-TB (i.e., patients with any other medical condition). A linear mixed model (LMM) was fit to estimate the change in HCW time following the intervention. Results: A total of 140 and 143 HCW participated in the TAMs during the Evaluation and Strengthening phases, respectively. Results from intervention facilities showed an increase of 9% (95% CI: 3%, 15%) in the proportion of HCW time spent on LTBI-related services, but with a corresponding change of -11% (95% CI: -21%, -1%) on active TB services. There was no change in the proportion of time spent on LTBI care in control facilities; this remained low in both phases of the study. Discussion: Our findings suggest that additional HCW personnel will be required for expansion of LTBI services to ensure that this expansion does not reduce the time available for care of active TB patients.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".