Tracking Implementation Outcomes of an Intensive Case Management Program for HIV: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Implementation science investigates the processes and factors that influence the successful adoption, implementation, and sustainability of interventions in many settings. Although conventional research places significant emphasis on the advancement and effectiveness of interventions, it is equally imperative to comprehend their performance in practical, real-life situations. Through outcome tracking, implementation science enables researchers to investigate complex implementation dynamics and go beyond efficacy, identifying the various aspects that contribute to the success of interventions. OBJECTIVE: This study aims to evaluate the implementation outcomes of TAIBU's intensive case management (ICM) model tailored for African, Caribbean, and Black communities living with HIV in the Greater Toronto Area. Specifically, it seeks to assess the fidelity, reach, and sustainability of the ICM program. Fidelity monitoring will ensure adherence to program protocols and consistency in service delivery, essential for achieving desired health outcomes. Reach assessment will examine the program's capacity to reach the target population, including demographic coverage and engagement levels among African, Caribbean, and Black individuals. Sustainability assessment will explore the determinants influencing the longevity and impact of the ICM program. METHODS: The study uses a mixed methods approach, where we will use probing questionnaires, interviews, and focus-group discussions to gather program performance and engagement data, in-depth insights, and perspectives from the implementation team responsible for delivering the ICM intervention. The collected fidelity and reach data through questionnaires will be analyzed using appropriate statistical techniques, such as descriptive statistics, to summarize the responses and identify patterns and trends within the data. Sustainability data collected through the interviews and focus groups will be analyzed and organized based on the Consolidated Framework for Implementation Research, which provides an organized way to identify and comprehend the determinants influencing implementation outcomes. RESULTS: The study commenced in January 2024, and initial data collection is expected to be completed by December 2024. As of September 2024, we have enrolled 5 participants. CONCLUSIONS: This study will significantly contribute to improving the implementation of the ICM program. By conducting a study in an organizational or institutional setting, researchers can acquire valuable insights into the implementation process from those who are directly involved. The information gathered will inform strategies for improving implementation effectiveness; removing impediments; and enhancing the overall quality of the ICM program for African, Caribbean, and Black individuals living with HIV. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57452.
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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 | 0.115 | 0.087 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.014 |
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".