Challenges of Hospital Incident Command System (HICS) from Experts’ Perspectives: A Qualitative Research
Bibliographic record
Abstract
Background: Hospital Incident Command System (HICS) is one of the most valid incident command systems for preparing and increasing efficiency of hospitals. With regard to hospitals’ key roles in Medical Incident Management, the present study aims at obtaining experts’ ideas for investigating challenges of establishment of HICS in Iran’s hospitals. Methodology: The present study is qualitative one conducted via the semi-structured interviewing method. Interviews were conducted on 23 experts selected from HICS in the Medical University, Red Crescent Society and Social Security Organization in 2016 so that after recording each interview, they were transcribed and, then, the raw data were reduced and organized via the content analysis technique. Results: According to findings of the present study, since the HICS is established based on the principles that ensure the effective deployment of resources on one hand and decrease the disorder in policy making and the operations of responding organizations, on the other hand, the point of view of most participants in this study showed that the HICS in Iran is not implemented properly. The studies showed that consistency of this system with existing management structure in hospitals cause internal and external barriers to its implementation. Conclusion: Based on the present results, the most important cases causing inefficiency of HICS in Iran are as follows: complete lack of understanding of HICS’s components and features, lack of adequate training of the staff, and lack of localization HICS in Iran. Thus, appropriate planning, necessary intra-and inter-organizational coordination in incidents, reinforcement of forces by appropriately organizing them, supply of required training, suggestion of long-term strategies, and finally design of a HICS by applying components of the Quality Management System with regard to conditions in Iran seem necessary. Keywords: Challenges, Hospital Incident Command System (HICS), Qualitative Research, Semi-Structured Interview
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".