"It's for sure conscious that we stay central": Barriers in Accessing Preventive Resources for Chlamydia among Youth in Southwest Calgary
Bibliographic record
Abstract
Chlamydia is the most commonly reported notifiable infection among youth (15-24 years) in Alberta.However, controlling infections has been challenging for Calgary, which has the youngest population of major cities in Canada.Health reports indicate that despite the high chlamydia incidence among youth, they are not using preventive resources as expected, thus the need to examine the barriers in accessing such resources among youth in Southwest Calgary.The researcher postulated that there are spatial dimensions to the social barriers to accessing preventive resources, and that social and spatial factors work synchronously to hinder access.Opinions about the potential impact of social media dating applications on sexual activities are also examined.Data collection, which was guided by a Social Ecological Model, followed an exploratory sequential mixed-methods design.In-depth interviews were conducted with 23 youth and 4 service providers.Thereafter, a survey of the barriers was conducted with a sample of 382 youth.Thematic and descriptive statistical analytical methods were used to examine qualitative and quantitative data respectively.Geospatial analyses of the patterns of facility non-use and the spatial distribution of barriers were also conducted.Although 79.8% of youth had heard about a preventive resource, only 41.4% had used any resource before.The principal barriers reported were stigma, intimidating healthcare spaces, and uneasiness accessing resources.There was an inverse relationship between geographic distance and access for the communities on the extreme western segment of the study site.The efficacious combination of qualitative, quantitative, and geospatial analytical methods to examine the barriers to sexual health resources among youth is an indication of the practicality of merging traditional qualitative and quantitative methods with geospatial methods to community health inquiries.Lastly, 44.5% of survey participants agreed that social media dating applications influenced sexual activities to a very great extent, thereby providing additional evidence to inform the ongoing social media-sexual health discourse in the health geography and public health arena.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".