Bibliographic record
Abstract
Introduction: Quality circle (QC) consists of people working in a common work area, coming togethervoluntarily to identify, analyze and solve various quality related problems within their area.Methodology: The exploratory study was done in a hospital in India that was part of large public sectororganisation that ran the program of QC across its institutions and also in its non-core areas like the hospitals.Primary data was collected from the staff working in the hospital that was part of quality circles.Results: Most of the QC members reported an improvement in terms of the Job satisfaction andaccomplishment that was measured on the 6 parameters. The perception of the Non-members about thegeneral organizational climate was on the lower side in comparison to the members ofthe QCs. The membersof the QC seemed to fare better than the Non- members.Discussion and Conclusion: If the top and middle management develop faith and conviction in the efficacyof the QC program, then rolling this out is not an issue at all. Most of the members were appreciative of thefact that program of this kind was great learning experience. There was a unanimous agreement on the factthat this activity should continue and be extended to the whole of the organization in the whole country.The findings suggested that the employees saw positive changes after becoming part of QCs in regard totheir personal growth and they also perceived positive changes in certain important organizational aspectswhich helped them to function effectively.The organizational climate seemed to have improved with QC as reflected in job satisfaction and sense ofaccomplishment between QC members and non-Members.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".