A study to assess the effectiveness of a structured teaching programme on knowledge and practice of safe insulin administration among nurses in a tertiary care hospital: A pre-post design
Bibliographic record
Abstract
Background and objective: Diabetes Mellitus (DM) is not only a health issue but also an economic issue in India. Incorrect insulin injection techniques can lead to side effects such as pain, lipohypertrophy, and poor glycemic control. We designed the present study to assess nurses’ knowledge about safe insulin administration and evaluate the role of a planned teaching programme on knowledge and practices of safe insulin injection techniques in a group of nurses in tertiary care hospital.Methods: This is a pre-post design to study the effectiveness of the structured training programme - one hour of didactic lecture followed by demonstration of safe injection practices. Demographic data and knowledge about safe insulin practices were collected at baseline. We conducted two post training assessments–day one and three months after training. The injection practices were assessed using a check-list. We used the random effects linear regression model to identify factors associated with change in scores over these three observations.Results: The mean (SD) scores for insulin knowledge at baseline was 6.81 (2.28). It significantly increased to 16.85 (1.84) immediately after training (p < .001). These scores reduced significantly after three months compared with post-training scores (14.18 [2.14]; p < .001). A significantly higher proportion of nurses had used re-suspension technique for insulin injection after three months (76.3% vs 52.5%, p = .003) and cleaned the injection site with alcohol swab before injection (93.8% vs. 75.0%, p = .001). On an average, knowledge scores changed by -0.15 (95% CI: -0.29, -0.02; p = .03) with each unit increase in age (years). The average score in nurses with a degree was significantly higher compared with those who had a diploma (1.02, 95% CI: 0.28, 1.76; p = .007).Conclusions: The study demonstrated that insulin injection practices improve with adequate guidance and information. However, there is a need to have a regular training programme to sustain the practices. Certain practices such as site rotation and assessing lipo-hypertrophy, and the relation between these two should be emphasized in these sessions.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".