Household Socio-Cultural and Economic Predictors of Drug and Substance Abuse among High School Students in Kisumu East Sub County, Kisumu County –Kenya
Bibliographic record
Abstract
PURPOSE: The household socio-cultural and economic predictors of drugs and substance abuse among high school students were investigated in Kisumu East Sub-County, Kisumu County. STUDY AREA DESCRIPTION: The study was conducted in Kisumu East Sub County in Kisumu County within the community set up. The area has approximated total population of 220,997 with an area of 141.6 sq.km density of 1560 per sq.km with a total number of 61,388 households. It is made up of five wards and 12 village units. The area has a total of 33 secondary schools with a total population of 26,000 students out of which 12,800 are males and 13,200 females. Out of 33 schools, 28 are day schools drawing students from the community. The main economic activities are quarrying, motorcycle ride, small scale businesses and farming. There is high rate of drop out of school among students due to the availability and accessibility of drug and substance abuse in the area as well as the geographical location. There are homes where bhang as well as local brew is sold at affordable cost. METHODS AND/OR TECHNIQUES: This was a descriptive cross-sectional study. The study targeted high school students aged 15-25 years learning within the study area. Sample size of 434 was calculated using Yamane formula and the participants selected through Snow ball, random, cluster and stratified sampling. Demographic characteristics were summarized using tables while inferential statistic done using Chi square, binomial logistic regression and multiple regression. Data collection done through observation and questionnaires. P value < 0.05 was considered statistically significant. RESULTS: Parental expectations, psychological disorders or mental problems, family background, leisure activities and festivities, number of siblings in the family, cultural beliefs and practices, birth position of the student, family shock and the need to treat certain ailments were found to be significantly associated with the abuse of drugs and substance (p <0.05). CONCLUSION: Lack of parental/guardian’s supervision and monitoring of the students movements, poor parent-child attachment due to inadequate family time, no communication on the dangers of drugs and substance abuse to students by the parents and high academic expectations by parents/guardians from students are the major contributors of drugs and substance abuse among students.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".