Facilitators of modularity in healthcare services: An interpretive structural modeling approach
Bibliographic record
Abstract
The purpose ofthis paper is to perform structural analysis of facilitators of modular architecturein healthcare services by applying interpretive structural modeling (ISM).Inputs were taken from healthcare industry experts and academicians inidentifying and understanding interdependencies among facilitators of modulararchitecture in healthcare services. Further these interdependencies arestructured into a hierarchy in order to derive structural models to deliveruseful insights for theory and practice. Using the ISM approach the facilitatorsof modularity in healthcare services were clustered according to their drivingpower and dependence power. Patient centricity is at the bottom level of thehierarchy implying highest driving power and requires higher attention todeliver quality care outcomes. Facilitators like value dense environment, knowledgeand competence, goal alignment and le-agile strategies have medium driverand dependence powers. The study added insights to the theory of modularsystems. Theauthors recognize that modularity helps in enhancing the patientcentric orientation. The findings provide potentially important information tohealth service managers and providers, enabling them to understand therequisites of modular architecture. This is the first study exploring therelationships between facilitators of modularity in healthcare services. Thestudy complements literature on service modularity with reference to specializedcare unit of maternity services.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".