Mapping the strategies to address the gender gap in youth clinic utilization in Sweden; explorative survey and follow up interviews
Bibliographic record
Abstract
BACKGROUND: Despite Sweden’s universalist health model and a long-standing network of youth clinics offering free sexual and reproductive health (SRH) services, boys and young men (BYM) remain significantly underrepresented among clinic users. This gender gap is driven by structural and cultural barriers, including masculine norms, female-oriented service environments, and limited institutional guidance for engaging male adolescents. While some clinics have piloted strategies to improve BYM’s access, these efforts have not been systematically documented or evaluated. The aim of this study was to systematically map and analyze the strategies used by Swedish youth clinics to improve access and participation among BYM. METHODS: This study employed an exploratory mixed-methods design, combining an online survey with follow-up unstructured interviews. The survey was distributed to all 240 youth clinics in Sweden between September and November 2024, with 75 responses representing 110 clinics (45% clinic-level response rate). Quantitative data were analyzed descriptively, while qualitative data from open-ended responses and interviews (24 interviews) were analyzed using inductive content analysis. RESULTS: BYM accounted for 9.5% of clinic visits, with 87% of clinics expressing dissatisfaction with this level. While 58% of clinics reported using strategies to attract BYM, only 34% of those using strategies evaluated their effectiveness, and institutionalization was limited. Four main strategy categories emerged: (1) staff capacity-building for male-inclusive care, (2) environmental adaptations to create inclusive spaces, (3) digital outreach and marketing to enhance visibility, and (4) collaboration with schools and community settings to reach BYM. Clinics using strategies were more likely to report increased male attendance, though implementation remained inconsistent and often project-based. CONCLUSIONS: This study provides the first national-level mapping of strategies to engage BYM in Sweden and contributes to global discussions on inclusive, adolescent-centered SRH care. Swedish youth clinics largely operate within a gender-neutral framework that fails to address BYM’s specific needs. A shift toward gender-responsive care, anchored in policy, training, and service design, is urgently needed. National coordination, standardized guidelines, and equity monitoring are essential to ensure that BYM benefit equally from youth health services. TRIAL REGISTRATION: Not applicable.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".