Establishing an ISO 10001-based promise in inpatients care
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to explore ISO 10001:2007 in planning, designing and developing a customer satisfaction promise (CSP) intended for inpatients care. DESIGN/METHODOLOGY/APPROACH: Through meetings and interviews with research participants, who included a program manager, unit managers and registered nurses, information about potential promises and their implementation was obtained and analyzed. A number of promises were drafted and one was finally selected to be developed as a CSP. FINDINGS: Applying the standard required adaptation and novel interpretation. Additionally, ISO 10002:2004 (Clause 7) was used to design the feedback handling activities. A promise initially chosen for development turned out to be difficult to implement, experience that helped in selecting and developing the final promise. Research participants found the ISO 10001-based method useful and comprehensible. PRACTICAL IMPLICATIONS: This paper presents a specific health care example of how to adapt a standard's guideline in establishing customer promises. The authors show how a promise can be used in alleviating an existing issue (i.e. communication between carers and patients). The learning can be beneficial in various health care settings. ORIGINALITY/VALUE: To the knowledge, this paper shows the first example of applying ISO 10001:2007 in a health care case. A few activities suggested by the standard are further detailed, and a new activity is introduced. The integrated use of ISO 10001:2007 and 10002:2004 is presented and how one can be "augmented" by the other is demonstrated.
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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 | 0.086 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".