Collectivism and Customer Orientation among Nurses: A Case Study in Iran
Bibliographic record
Abstract
The mission of organizations is to provide good service for the customers. Weakness in building collectivism morale leads to forgetting group needs and subsequently organizational objectives and customer needs and reduces the patients’ referral. On the same ground, the present study was carried out to investigate the relationship between collectivism and customer orientation in nurses’ staff which is the most effective hospital staff. The present analytical study was conducted in 2013 on a group of 200 nurses in a specialty hospital in Tehran. The instruments for gathering data include nurses’ demographic information (gender, age, marital status and work experience), collectivism questionnaire (10 questions in three dimensions of beliefs, values and norms), and the checking list of nursing staff’s extent of customer orientation (27 questions in 8 aspects). Data analysis was done by SPSS.19 and Spearman’s correlation tests, Chi Square, and single-sample T-test.The results showed that nursing staff had high collectivism morale. Among aspects of customer orientation, the highest and the lowest mean and standard deviation were respectively related to employees’ honesty (2/87±0) and employees’ appearance (1/71±0/66). There was a significant relationship between the two elements of customer orientation and collectivism (002/0=P). In addition, apart from the two dimensions of service assurance and employees’ appearance, significant relationships were observed between other aspects of customer orientation and collectivism.Regarding the relationship between collectivism and customer orientation in nursing staff, it can be said that customer orientation in an organization can be promoted by reinforcing collectivism morale in employees. Offering educational programs for promoting collectivism morale and subsequently promoting customer orientation can lead to providing good customer service.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".