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Record W4206168752 · doi:10.26443/ijwpc.v9i1.320

Peer support for adolescent girls living with scoliosis: finding a digital community

2022· article· en· W4206168752 on OpenAlexaffvenue
Marie Vigouroux, Kristina Amja, Richard Hovey

Bibliographic record

VenueInternational Journal of Whole Person Care · 2022
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyScoliosisAdolescent developmentDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Introduction : Scoliosis in a condition where a curve develops in the spine. Adolescent girls affected by scoliosis are significantly more likely to require treatment such as bracing or surgery than their male counterparts. Curvy Girls is a peer support group for adolescent girls with scoliosis that allows them to engage with each other in a safe environment. Objectives : This study endeavours to explore the experiences of adolescent girls living with scoliosis who are Curvy Girls members and understand how this peer support group has affected their experience. Approach : Sixteen participants were recruited through a senior board member of Curvy Girls. Data was gathered through semi-structured interviews with open-ended questions, transcribed verbatim, and analyzed using an applied philosophical hermeneutic approach, a practice of uncovering insights from transformational conversation. Findings : We found that the participants’ sense of belonging to Curvy Girls did not depend on their level of involvement with the group. Whether they were leaders in their in-person local group, or simply following the organization on social media, seeing themselves represented allowed the participants to feel like they belonged to the group. Future Directions : These findings may help clinicians, healthcare professionals, and peer support organisations deepen their understanding of the perspectives of this specific population. This transformed understanding could lead to the instauration of care and services that are better adapted to this population’s needs, resulting in lessening the burden of the condition on the individual and their support system.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.048
GPT teacher head0.328
Teacher spread0.280 · 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
Published2022
Admission routes2
Has abstractyes

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