ISO 10004-BASED MEASUREMENT AND INTEGRATIVE AUGMENTATION IN A HEALTH CARE CONTINUUM
Bibliographic record
Abstract
This paper investigates an application of ISO 10004 in a specific care continuum assumed to be an integrated health care case. It also illustrates the integrative augmentation of ISO 10001- and ISO 10002-based promise and feedback systems. An emergency and inpatient care continuum within a Canadian hospital was investigated by interviewing nurses and managers. Patients' service encounters with the care and support providers were examined and the existing measurement activities were studied. Steps for customer satisfaction measurement along the continuum were defined. Sources to determine patient expectations were identified and the measurement activities, such as a survey encompassing all stages within the care continuum, were developed. Research participants were interviewed again to verify the usefulness of the developed measurement activities. The presented work depicts the relationships among the aspects of customer satisfaction, key principles of integrated care and ISO 10004. It is one of the first examples of an application of ISO 10004 and the integrative augmentation of systems standardized by the ISO 10000 customer satisfaction series in health care. This paper is a revised version of Khan et al. (2017).
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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 | 0.030 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".