Patient satisfaction: evaluating nursing care for patients hospitalized with cancer in Tehran teaching hospitals, Iran
Bibliographic record
Abstract
Background: Patient satisfaction is used as an important indicator of quality care and is frequently included in healthcare planning and evaluation. A cross sectional study was conducted to examine the relationship between cancer patients’ satisfaction with nursing care in order to assist nurses in defining more clearly their roles in 10 government teaching hospitals in Tehran, Iran. Method: A proportional stratified sampling method was used. Data was collected via validated Patient Satisfaction Questionnaire (PSQ) within a 3 month period. Result: The majority of respondents was males (52.3%). The overall median age of respondent was 50 (Inter-quarter range, 26), ranging from 14 years old to 85 years old. The findings revealed that a vast majority of these respondents (82.8%) was satisfied with the nursing care provided to them, while the others (17.2%) were not. There was a significant relationship between patients’ satisfaction and University’s hospital, types of treatment (P?0.05). Also; the University’s hospitals was the best predictor for level of satisfaction. Conclusion: This study found that most of the respondents were satisfied with the nursing care, though they suggested some improvements especially with respect to interpersonal relation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".