8 A Youthful Take on Community-Based Healthcare
Bibliographic record
Abstract
Teens face unique developmental risks that traditional healthcare often does not address. School- and community-based youth health clinics have taken variable approaches to this problem. There is a lack of evidence on the best way to provide care to this population and a lack of understanding on what youth feel would be helpful. This study explores diverse and at-risk youths’ perceptions of current healthcare access and desired services. Specifically, this study asks, “What community or school-based resources do high school students aged 14–18 perceive as valuable in improving their health?” With this information, physicians and other healthcare providers will be able to provide more pertinent and effective care. Study participants were high school students aged 14–18 inclusively. Participants were recruited with posters and community partners. Focus groups happened in respective schools and community organizations. Participants completed an intake-survey before taking part in focus groups. Survey data was compiled quantitatively and focus group transcripts underwent inductive thematic analysis. Four focus groups were conducted, each consisting of 4–8 youths (n=28). Participants identified primarily as female (79%), heterosexual (75%), and Caucasian (54%), with grade point averages between 60 and 79 (64%) and an average age of 16.7 years. Participants had only a general understanding of what health care professionals do and how to access them. Teens valued simplicity in seeking care, such as convenient location, short wait times, no appointments, and no financial cost. Teens wanted healthcare to be a personal experience. They wanted to see healthcare professionals they trust, and who they could relate to. Teens also wanted to see diverse staff in terms of gender and ethnicity, but generally want to see younger staff. They wanted to access a single source for a multitude of problems and did not want to be referred onward unnecessarily. Participants were concerned about the stressors associated with their developmental stage, social media, and bullying. Finally, teens wanted healthcare to be private and discretely accessible. A diverse group of youth have reaffirmed the importance of known health issues with new perspectives and identified new potential areas of focus. These ideas have actionable implications for accessibility, familiarity, comprehensiveness, mental health and confidentiality in the context of community adolescent health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".