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Record W3214415488 · doi:10.32920/ryerson.14645766.v1

Case study of child, parent and teacher perceptions of a school-based health clinic in the model schools paediatric health clinic in the model schools paediatric health initiative of the Toronto District School Board

2021· preprint· en· W3214415488 on OpenAlexaffabout
Jacqueline Chan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentYork University
Fundersnot available
KeywordsThematic analysisChild healthMedicineHealth careFamily medicinePerceptionNursingSchool healthQualitative researchGrounded theoryMedical educationPediatricsPsychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate child, parent, and teacher perspectives of the role of a Toronto school-based health clinic (SBHC) in health care provision, as well as their experiences of accessibility, and comfort in use. This qualitative case study of a SBHC in the Toronto District School Board’s Model Schools Paediatric Health Initiative (MSPHI) uses thematic secondary data analyses informed by a grounded theory approach. The results of this study provide evidence that the SBHC plays a key role in the provision of physical health care for children; reduces health-related school absences; addresses OHIP-related barriers; and enhances the coordination of health care services. Children’s experiences of comfort over time remained stable; however, children’s perceptions of accessibility improved over time which aligned with their increased SBHC utilization. Key terms: children, health services, school-based health clinic, Model Schools Paediatric Health Initiative

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.170
GPT teacher head0.483
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2021
Admission routes2
Has abstractyes

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