Moving beyond illness: examining the experiences of women living with HIV and other chronic illnesses in a peer-led community-based dance program
Bibliographic record
Abstract
Peer-led community-based physical activity programming has gained popularity owing to its potential to improve health, wellbeing, and social connection, particularly within marginalised communities. The Positively Dance program was a community-based participatory research project designed by and for women living with HIV and/or other chronic illnesses. The purpose of this qualitative study was to examine participants’ perceptions and experiences of engaging in the peer-led dance program, including the factors that shaped their access to and engagement in classes. Ten participants (six women living with HIV and four women living with other chronic illnesses) participated in semi-structured interviews. Data were analysed using reflexive thematic analysis. Three themes were identified and interpreted, including 1) how dance can address existing community needs among women living with HIV and/or other chronic illnesses, 2) the embodied experiences of dance and its connection to joy, hope, and pain, and 3) the role of affinity spaces and non-illness identifying places in promoting social inclusion. Our findings highlight the complexities involved in navigating affinity spaces for women living with HIV and/or other chronic illnesses and the role of non-illness identifying places in centring joy, rather than suffering.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".