Investigation of the Relationship between Perfectionism withPost-Cesarean Pain and Anxiety
Bibliographic record
Abstract
Background and Objectives: Post-cesarean pain and anxiety is associated with several complications and it is very important for maternal and neonatal health care and breastfeeding. Some Studies are indicative of a relationship between psychological factors and pain. The aim of this research was to investigate the relationship between perfectionism and post-cesarean pain and anxiety. Methods: This correlational descriptive analytical study was carried out on 70 eligible pregnant women who referred to Omolbanin hospital of Mashhad city for elective cesarean section. Sampling was performed using consecutive method. Data were collected using Ahvaz Perfectionism Questionnaire (APS) and the Spielberger State-Trait Anxiety Inventory (2-3 hours before cesarean section). Two hours after cesarean section, Spielberger questionnaire was used and to assess the patients’ pain, short-form McGill Pain Questionnaire (SF-MPQ), was used. Data were analyzed by Chi-square, Spearman, and Pearson correlation tests at the significance level of p<0.05. Results: In this study, there was a significant correlations between perfectionism score and trait anxiety (r=0.51, p<0.001) and state anxiety before (r=0.41, p=0.001) and after (r=0.43, p=0.001) the cesarean section. Although there was significant correlation between perfectionism score and sensory dimension of SF-MPQ (r=0.31, p=0.017), no significant correlation was observed with emotional dimension of SF-MPQ (r=0.14, p=0.319(. Conclusion: The results of this study showed that perfectionism is associated with anxiety and pain after cesarean section; therefore, cognitive behavioral counseling is recommended for perfectionists during pregnancy.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".