Three Major Interrelated Factors Contributing to Homelessness Issue among Former Prisoners in Malaysia
Bibliographic record
Abstract
Homelessness issue among former prisoners in Malaysia upon their release is of great concern. Hence, this study aimed to identify the predominant factors influencing homelessness issue among former prisoners in Malaysia. Imprisonment is usually assumed to be a negative life event and can act as a hindrance for the former prisoner to successfully integrate after being freed from prison. Imprisonment and past criminal records are the biggest contributors to becoming homeless. This is a fact because imprisonment causes the former prisoners to lose his source of income, personal belongings, ability to seek shelter and personal relationships due to family rejection, addiction and unemployment. This study was based on the Ecological Model of Homeless by Nooe and Patterson. The selection of this model was considered appropriate and aligned with the objectives of the study which aimed to identify the factors that lead to the life of the homeless among former prisoners. In this study, nineteen former prisoners, regardless of the type of offence committed, were selected using the snowball sampling method and were interviewed. The findings revealed that family denial, unemployment, and drug addiction were the three major interrelated factors that contribute to the homelessness issue among the former prisoners during their reintegration process. Housing security is a risk factor of homelessness.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".