Self Esteem and Organizational Commitment Among Health Information Management Staff in Tertiary Care Hospitals in Tehran
Bibliographic record
Abstract
BACKGROUND: Self esteem (SE) and organizational commitment (OC)? have significant impact on the quality of work life. AIM: This study aims to gain a better understanding of the relationships between SE and OC among health information management staff in tertiary care hospitals in Tehran (Iran). METHODS: This was a descriptive correlational and cross sectional study conducted on the health information management staff of tertiary care hospitals in Tehran, Iran. A total of 155 participants were randomly selected from 400 staff. Data were collected by two standard questionnaires. The SE and OC was measured using Eysenck SE scale and Meyer and Allen's three component model, respectively. The collected data were analyzed with the SPSS (version 16) using statistical tests of of independent T-test, Pearson Correlation coefficient, one way ANOVA and F tests. RESULTS: The OC and SE of the employees' were 67.8?, out of 120 (weak) and 21.0 out of 30 (moderate), respectively. The values for affective commitment, normative commitment, and continuance commitment were respectively 21.3 out of 40 (moderate), 23.9 out of 40 (moderate), and 22.7 out of 40 (moderate). The Pearson correlation coefficient test showed a significant OC and SE was statistically significant (P<0.05). The one way ANOVA test (P<0.05) did not show any significant difference between educational degree and work experience with SE and OC. CONCLUSION: This research showed that SE and OC ?are moderate. SE and OC have strong correlation with turnover, critical thinking, job satisfaction, and individual and organizational improvement. Therefore, applying appropriate human resource policies is crucial to reinforce these measures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".