Challenges to immunization: the experiences of homeless youth
Bibliographic record
Abstract
BACKGROUND: Homelessness is a critical social issue, both a product of, and contributing to, poor mental and physical health. Over 150,000 young Canadians live on the streets. Homeless youth experience a high incidence of infectious diseases, many of which are vaccine preventable. Early departure from school and limited access to public health services makes them a particularly vulnerable high-risk group. This study explores challenges to obtaining essential vaccines experienced by homeless youth. METHODS: A qualitative research study to explore knowledge, attitudes, beliefs, and experiences surrounding immunization of hard-to-reach homeless youth was designed. Participants were recruited for focus groups from Phoenix House and Shelter, a non-profit, community-based organization assisting homeless youth in Halifax, Nova Scotia, Canada. An experienced facilitator guided the recorded discussions. Transcripts of audiotapes were analyzed using a constant comparative method until data revealed a set of exemplars and themes that best captured participants' knowledge, attitudes, beliefs and experiences surrounding immunization and infectious diseases. RESULTS: Important themes emerged from our analysis. Considerable variability in knowledge about immunization and vaccine preventable diseases was found. The homeless youth in the study had limited awareness of meningitis in contrast to a greater knowledge about sexually transmitted infections and influenza, gained during the H1N1/09 public health campaign. They recognized their poverty as a risk for contracting infectious diseases, along with their inability to always employ known strategies to prevent infectious diseases, due to circumstances. They showed considerable insight into the detrimental effects of poor hygiene, sleeping locations and risk behaviour. Interviewed homeless youth regarded themselves as good compliers of health professional advice and offered valuable suggestions to improve immunization in their population. CONCLUSIONS: To provide effective public health interventions, it is necessary to consider the knowledge, attitudes, beliefs, and experiences of hard to reach, high risk groups. Our study shows that homeless youth are interested and capable in discussing immunization. Active targeting of homeless youth for public health immunization programs is needed. Working collaboratively with non-profit organizations that assist homeless youth provides an opportunity to increase their knowledge of infectious risks and to improve immunization strategies in this vulnerable group.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".