Developing a Conceptual Model of the Hospital Incident Command System (HICS) Via Quality Improvement Models in Iran
Bibliographic record
Abstract
Achieving the goals of organizations requires an appropriate model for performance evaluation. Applying globally accepted methods for administration of hospital incident command system (HICS) can build a new tool for improving the quality of evaluation of real disasters and incidences. The present study seeks to develop a model through the implementation of a quality management system. This applied study was conducted in two steps in 2016. First, data collection were collected from library-printed and electronic references related to the purpose of the research .According to the inclusion and exclusion criteria, 28 articles having conducted on HICS and 50 articles on the quality management system were selected. In the next step, interviews were conducted with 23 experts and the themes were obtained through qualitative study and content analysis. Then the data were extracted. According to qualitative interviews, two themes of the proposed quality improvement models in health centers and the appropriate model of the HICS were extracted. Then, the qualitative elements of the models were determined for overlapping the HICS based on the similarities and differences in the study models. Most important dimensions examined was organization management system, management and leadership, customer focus, personal of development plan, information & communication management, non-conformity, improvement, audit. Since the HICS faced some limitations, such as insufficient attention to quality improvement, incompatibility of this system with the management structure in hospitals, weakness in system communication, and lack of a native model in Iran, this article attempts to develop a conceptual model that has the most common features among the models for filling the gaps in evaluating the HICS.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".