Giving Voice to Young People: Evocative Autoethnography as a Method of Researching Personal Experiences Connected o Adolescent Idiopathic Scoliosis. A discursive article
Bibliographic record
Abstract
Background: Adolescent idiopathic scoliosis (AIS) is one of the most common spine disorders amongst young people, with a rich body of biomedical research mainly focused on the effects of interventions.However, there still needs to be more research regarding personal experiences connected to this complex health condition, including people's views, preferences, or attitudes, even though they constitute evidence-based practice and person-centred care as understood today.This work aims to present that addressing this serious evidence-to-practice gap is needed and possible via qualitative research, specifically evocative autoethnography. Materials and methods:Evocative autoethnography will thus be introduced, described, and reviewed in terms of its usefulness in AIS research, discussing two autoethnographies addressing and based upon scoliosis experiences of the first author of this work, that can serve as examples of how this method can be utilised. Results and conclusions:Evocative autoethnography allows analysing and often exploring such important issues related to AIS as body image, emotional difficulties, or psychological distress, informing health care professionals and rehabilitation practice.This is especially important in the field of AIS, where the personal consequences, coping with the condition, as well as a choice of treatment adherence are often profound and challenging for the patients and their families.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".