Altruism the Essense of the Iranian Nurses’ Job Satisfaction: A Qualitative Study
Bibliographic record
Abstract
Skillful and efficient human resource is one of the most important tools for reaching the organizational targets and it is almost impossible to reach the predetermined goals and success without having skillful human resources. Therefore, having a study on the personnel's job satisfaction is recommended for all of the organizations. Since the health organizations are among the most important organizations of any country, paying attention to the nurses' job satisfaction as the main providers of the health care services gets very important. In fact, their attempts guarantee the efficient human resources' health in the society. Understanding the Iranian nurses' experiences of their job satisfaction. The present paper studies the implicit and explicit aspects of the clinical nurses' job satisfaction. The needed information is collected via interviews, and then the participants' contextual data is analyzed by the qualitative content analysis. The research results introduce the altruism as the foundation for the nurses' job satisfaction. Altruism is composed of three categories of the patient advocacy, spiritual job satisfaction, and professional commitment. Altruism has made the nurses deliver the required health cares to the patients with all their love, while their profession has many difficulties. Job satisfaction resulted from altruism is experienced as a pleasant feeling along with enjoyment resulted from addressing the needs of a patient who looks forward to the nurse's advocacy. According to this kind of job satisfaction, the nurse's professional commitment is to advocate for the patient. Also, the research results show that spirituality is the inseparable component of altruism and it has a vital role in the nurses' job satisfaction. The spirituality helps the nurses to deliver targeted acts and interventions.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".