THREE EXAMPLES OF INTEGRATIVE AUGMENTATION IN HEALTH CARE AND ENGINEERING EDUCATION SERVICES
Bibliographic record
Abstract
Integrative augmentation of standardized management systems, though proposed more than ten years ago, remains underexplored. This paper presents three examples of such integrative augmentation in health care and engineering education services in Western Canada. The first example illustrates how ISO 10003 could be selectively and comprehensively used in an ISO 10002-based process for handling concerns in a provincial health care system. The second shows a case of ISO 10004 augmenting an ISO 10001-based “Customer Satisfaction Promise” in an inpatients care unit of a hospital. Finally, different instances of integrative augmentation involving ISO 10001, 10002 and 10004 in engineering education are reported in the third example. This paper is likely the first to present examples of ISO 10000 systems being augmented by other ISO 10000 subsystems, and aims to promote the integrative use of augmenting management system standards by showcasing various approaches to the related augmentation in two different service industries, namely health and education.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".