Developing Environmental Scanning in Iranian Healthcare: A Comparative Review and a Proposed Model
Bibliographic record
Abstract
Context: In recent years, environmental scanning has attracted noteworthy attention within health research in healthcare organizations harnessing this technique to perform their operations. Objectives: This study aimed to compare environmental scanning models and provide a model for Iran’s health system. Evidence Acquisition: This qualitative and comparative research employed an applied purpose in four stages: Description, interpretation, juxtaposition, and comparison. The primary data collection tool was comparative tables to gather data by reviewing articles, documents, and books using scientific databases. The collected information was analyzed by the Beredy method. Results: The most significant models were presented by countries including Singapore, Canada, Iran, and the United States. Most health environmental scanning studies were conducted in countries such as Canada, Australia, the United States, and England. Notably, esteemed researchers such as Albright, Daft, Xue Zhang, Choo, Costa, and Nezhadi introduced influential environmental scanning models. Conclusions: Environmental scanning is a powerful tool in decision-making and strategic planning for organizations, fundamentally impacting their survival and progress. The healthcare system’s general model for environmental scanning is presented in five steps. Based on the results, the environmental scanning model can enable managers and strategic teams to identify risks, opportunities, constraints, and threats and determine suitable strategies for organizational growth and success.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".