Effectiveness of self-instructional module on selected alternative approaches to pain relief during labour among staff nurses at selected maternity hospitals, Bengaluru
Bibliographic record
Abstract
Introduction: Labor pain is the rhythmic pain of increasing severity and frequency due to contraction of the uterus at childbirth. Even though, delivery is a natural phenomenon, it has been demonstrated that the accompanying pain is considered severe/extreme in more than half of the cases. Supported labour by midwives without using pharmacological pain relief plays a vital role in managing labour pain. To aid in providing information to the mothers, the health care professionals such as nurses and midwives must have adequate knowledge regarding these alternative methods.Materials and Methods: A quasi experimental approach - one group pre-test post-test design was used in this study. 50 staff nurses who met with inclusion criteria were selected using non-probability purposive sampling technique. Informed consent was obtained from all the participants. The data was collected using self-administered questionnaire comprised of demographic variables and knowledge questionnaire. After pretest the self-instructional module on selected alternative approaches to labour pain relief was given and posttest was conducted after a week using same questionnaire tool.Result and Conclusion: The improvement mean score for overall knowledge score was 7.38 with the ‘t’ value of 9.6840 and found to be significant in the level of p<0.05. Hence it is evident that the self- instructional module is effective in improving the knowledge of staff nurses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".