Evaluation of the Establishment of a Public and Patient Involvement and Engagement Group to Support Clinical Trials in Pakistan: Protocol for a Mixed-Methods Study
Bibliographic record
Abstract
Background: Patient and public involvement and engagement (PPIE) in research is a collaboration between researchers, patients, and the public, enhancing research acceptability, relevance, and impact. There is a growing prevalence of PPIE in high-income country research; however, its integration in low- and middle-income countries (LMICs) remains poorly understood. Recognising this gap, the Ziauddin University Clinical Trials Unit in Karachi, Pakistan, launched a dedicated PPIE initiative in 2022. This study evaluates the engagement process and experiences of patient and public members and researchers to identify barriers and facilitators to participation within the PPIE group. Methods: The evaluation uses an explanatory sequential mixed-method design. First, the Public and Patient Engagement Evaluation Tool (PPEET) questionnaire will be administered online to group members, coordinators, and senior institutional leads. Insights from questionnaires will be further explored during semi-structured interviews, with questions guided by the Patient Engagement in Research (PEIR) framework, supplemented with analysis of project documentation. Study activities will be conducted in both English and Urdu. The study has been co-designed with PPIE members and is co-led with a public partner. Findings will highlight areas for improvement, inform best practices, and guide the development of more effective engagement strategies. Outcome: Although focused on a single group, this evaluation lays the groundwork for understanding PPIE practices in LMIC contexts. It provides valuable insights into developing equitable partnerships and improving patient-centred research. This study contributes to a growing body of knowledge, offering practical guidance for implementing PPIE in settings with unique socioeconomic challenges and cultural realities. The findings are expected to benefit the local research community and similar initiatives globally, particularly in regions with comparable challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.382 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".