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Record W2990752717 · doi:10.14485/hbpr.6.6.6

Teens' Perspectives on Barriers and Facilitators to Accessing School-based Clinics

2019· article· en· W2990752717 on OpenAlexaboutno aff
Catherine Charette, Colleen Metge, Ashley Struthers, Jennifer Enns, Nathan Nickel, Mariette Chartier, Dan Château, Elaine Burland, Alan Katz, Marni Brownell

Bibliographic record

VenueHealth Behavior and Policy Review · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsConfidentialityPsychologyQualitative researchMedical educationNursingMedicineComputer securityComputer scienceSociology

Abstract

fetched live from OpenAlex

Objective: This qualitative study explores teens' perspectives on facilitators and barriers to accessing school-based clinics, emphasizing the importance of youth self-report. Methods: We conducted in-person interviews with teens (N = 25) at 2 high schools that had school-based clinics in Winnipeg, Manitoba (Canada). We supplemented the interviews with a brief questionnaire administered to a sample of teens at both schools (N = 105). Data were iteratively coded and analyzed using NVivo. Results: Teens framed 5 clinic attributes that facilitated access: confidential, welcoming, judgment-free, validating and understanding, and fast/easy/convenient. Teens identified judgment from peers, fear and anxiety, hours/wait times, lack of privacy, and teens who gather at the clinic to socialize as factors that made the clinic hard to access or inhibited access entirely. Conclusions: School-based clinics were highly regarded by teens who used them. The most important factors underlying ease of use were assurance of confidentiality and a positive client-provider relationship that made teens feel safe and comfortable. However, considerable individual and structural barriers remain to ensuring the clinics are teen-friendly and accessible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.546
Teacher spread0.440 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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