FIRE DISASTER PREPAREDNESS AMONG STUDENTS IN KENYA MEDICAL TRAINING COLLEGES IN EASTERN KENYA
Bibliographic record
Abstract
Purpose: The major objective of the study was to determine the status of fire preparedness among students at Kenya Medical Training College. Methodology: This was a descriptive cross-sectional study design. The study populations were 1st and 4th year students in Machakos and Embu KMTCs. A sample size of 336 students was selected randomly in both campuses while stratified random sampling technique was used to sample students from departments and classes in each College. Data was collected using pre-tested questionnaires, focus group discussions and key informant interviews. All the data collected was entered into Statistical Package for Social Sciences (SPSS) version 20 and analysis done using descriptive and inferential statistics. Findings: Students were aware of the types of disasters which could affect them while in the college with 181 (54%) of the respondents knew the possible fire risk sources in the rooms. Majority 218 (64.9%) did not know the college fire safety policy guidelines, while (72%) stated that they were vulnerable to fire disaster. Majority 329(98%) said fire drills as safety measures were never practiced in these colleges. There was no significant association between students’ age, gender, religion, and year of study and fire disaster preparedness (p>0.05). Unique contribution to theory, practice and policy: There is need for the institutional fire policy to ensure students are trained of students on fire safety after admission. The data generated can be used by KMTC management through conducting periodic fire drills to keep students well prepared on fire preparedness and post their telephone numbers for the nearest firefighting equipment on the college notice boards, classrooms and in hostels
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".