Comparison of Laboratories Directors' and Assessors’ Opinions on Challenges and Solutions of Standardization in Iran: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: The quality medical laboratory services play a vital role in healthcare systems. Iran has set national standards based on the international standard ISO15189. These standards came into force in September 2007. Given the important role of both laboratories professional and assessors in the standardization, this study aims to compare and analyze medical laboratory directors' and assessors' opinions about this process, its challenges and relevant solutions. METHODS: This qualitative study was conducted on two populations in 2013. The first survey population consisted of 150 assessors. The second group consisted of directors working in medical laboratory settings. From all universities of medical sciences, 258 medical laboratories were randomly selected. Data were gathered using two open-ended questionnaires and analyzed using the thematic analysis. RESULTS: Challenges and relevant solutions regarding the standardization and standards, the assessment process and assessor, laboratories, external entities and contextual factors across laboratories directors and assessors were derived and compared. Both groups had a positive attitude towards the standardization process. However, they expressed some concerns regarding the process and accordingly proposed solutions to overcome the challenges. CONCLUSION: This study provides insights into the challenges and solutions of the standardization from two professional groups' viewpoint. These two factors are closely related and should be considered when implementing standards since a positive perception of them increases the likelihood of successful standardization. Similarities and divergences regarding challenges and solutions of the standardization, in turn, can provide insights into how this process can be improved and deserve policy makers' attention to continue the progress.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".