Assessment of Hand Hygiene Knowledge, Attitude, and Practice Among Health Sciences Students in Herat, Afghanistan: A Cross-Sectional Study [Response to Letter]
Bibliographic record
Abstract
We are sincerely grateful to Shinde, Islam, and Dakurah for their thoughtful engagement with our article, "Assessment of Hand Hygiene Knowledge, Attitude, and Practice Among Health Sciences Students in Herat, Afghanistan", and for their insightful comments that foster a critical academic debate. 1 The opportunity to clarify our methodological decisions and elaborate on the unique context of our research is one we welcome.Our study's primary objective was to establish a crucial baseline understanding of hand hygiene (HH) knowledge, attitudes, and practices (KAP) among the next generation of healthcare professionals in Afghanistan. 2 In a nation grappling with a fragile health system, such foundational data are indispensable for developing targeted educational curricula and effective, context-specific infection prevention and control policies.While we appreciate the methodological ideals raised, which represent the gold standard in well-resourced environments, we remain confident that our research design was a deliberate and ethically necessary adaptation to the severe realities of conducting research in a conflict-affected setting.3 The correspondents rightly note the limitations of our multi-site convenience sampling strategy.However, a rigid insistence on probability sampling in a setting such as Herat would be both impractical and ethically untenable.The prerequisites for such methods, namely a comprehensive and accurate sampling frame, are nonexistent due to decades of conflict and population displacement.4 Additionally, attempting to create one would have posed unacceptable security risks to both our research team and the participants.5 Consequently, our approach was not a shortcut but the only feasible and ethical pathway to gather vital preliminary data in a constrained setting.Similarly, while we acknowledge that our reliance on self-reported data likely inflated adherence rates due to social desirability bias (SDB)-a limitation we explicitly noted in our manuscript-we argue the finding is still valuable.6 The high score indicates that students have successfully internalized professional norms; the critical challenge, therefore, is addressing the systemic barriers that prevent the translation of knowledge into practice, such as inconsistent supply access and overwhelming workloads.7 The critique regarding our instrument validation is also appreciated.Our multi-step process, involving review by local experts and a pilot study yielding strong reliability, represented the most rigorous approach feasible where large-scale psychometric studies are not possible.Regarding our analytical choices, the use of a median split to categorize KAP scores was a deliberate decision appropriate for this exploratory study, as no universal benchmarks exist for this population; this method enhances interpretability by identifying predictors of relatively better or worse performance within our specific cohort.8 We must also respectfully correct
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".