Quality assurance in allied healthcare education: A narrative review
Bibliographic record
Abstract
Introduction: There is no standard methodology for outlining the intricacies of allied healthcare education (AHE) or its quality. The profound misconception is that quality assurance (QA) in AHE is used on a "voluntary" basis. Given the absence of statutory regulatory mechanisms such as accreditation, validation, and audit by the peripheral agencies concerning QA, adoption of QA measures in AHE is not consistent, and it results in producing a subpar allied health workforce. This paper analyzes the need to include QA measures as an essential domain in evaluating the effectiveness of allied health professional education programs. Method: A large database search was performed using pertinent terms, and a blueprint was developed for a meticulous literature review published between 2015 and 2021. Five hundred eighty-two articles were found and screened; a critical appraisal was performed for 22 peer-reviewed articles for relevant information. Results: The literature review identified the need to use academic domains such as leadership, planning, delivery, and feedback as QA criteria to evaluate the efficiency of education and training in allied health professional education programs. Instructors and facilitators for specific knowledge and skill development and a description of their roles should also be used in QA evaluation. Conclusion: Resources for effective learning and teaching in the allied healthcare domain are limited. This review highlights the significant need to include a QA system in AHE, considering the pivotal role of these students in supporting humankind, now and in the future. The findings contribute to the research by providing essential insights into current trends and focusing on existing research in AHE quality.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".