Effectiveness of Structured Teaching Programme on Knowledge regarding the Prevention and Management of Covid-19 among Housewives in selected area of Alappuzha District in Kerala, India
Bibliographic record
Abstract
The Coronavirus disease (COVID-19) is an infectious disease caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). It has been recognized as a pandemic by WHO, and the rate is succeeding in day by day as mostly as community spread. Even though the invention of vaccines has a great result, we must continue the essential public health actions to suppress transmission and reduce mortality. The purpose of the study is to identify the effectiveness of structured teaching programme on knowledge regarding the prevention and management of covid-19 among housewives in selected areas of Alappuzha district in Kerala. The research method adopted for this study is an interventional study of one group pretest posttest type was design. The study group consists of 50 housewives selected by convenient sampling technique. The researcher assessed the knowledge regarding the prevention and management of COVID-19 using a structured knowledge questionnaire followed by a structured teaching programme. The study revealed that on pretest only 44% of samples had a good knowledge, 30% had average knowledge and 26% had poor knowledge whereas during the posttest all the samples scored good knowledge level. The study revealed that the structured teaching programme was effective in improving the knowledge score among the housewives. The study pointed out a clear need for training programme with respect to a specific cluster of population emplaning upon their respective lifestyle, to improve the knowledge and compliance about risk and preventive measures. As a nursing professional, we have a crucial role creating awareness by innovative ways which should be adopted as one of the best practices to combat the spread of pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".