Video-assisted nursing intervention: It's effectiveness on bullying prevention measures and procedures among primary school teachers
Bibliographic record
Abstract
Background: Bullying commonly defined as an aggressive behavior that is intentional, repeated for a long time and involves an imbalance of power. It can have negative effects on children’s physical and psychological health and can even escalate to the tragedy of suicide. Aim: This study aimed to determine effectiveness of video-assisted nursing intervention on bullying prevention measures and procedures among primary school teachers.Methods: A quasi-experimental design with pre and post-test was used. Sample: Simple random sample of 100 primary school teacher was included. Settings: The study was carried out at four primary schools in Shebin-Elkom and menouf, Menoufia Governorate, Egypt. Tools: Tool one: teachers’ self-administered structured interview questionnaire (a) Demographic data (b) Teachers knowledge regarding bullying. Tool two: bullying preventive measures likert scale.Results: The study showed that after video-assisted nursing intervention there was statistically significant improvement in the knowledge of primary school teachers' about bullying compared to before nursing intervention. Also, there was statistically significant improvement in teachers prevention practices regarding bullying after video-assisted nursing intervention compared to before nursing intervention. There was positive correlation between teachers’ total knowledge scores and total practices scores about bullying post intervention.Conclusions: Implementation of video-assisted nursing intervention achieved significant improvements in the primary school teachers’ knowledge and practices measures regarding bullying prevention. Recommendations: Prominently utilizing video-assisted nursing intervention strategy in teaching bullying to promote children's health and improve knowledge and practices of teachers.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".