Exploring Textured Hair Care as a Meaningful Occupation: A Thematic Analysis
Bibliographic record
Abstract
Background. Current health and occupational therapy literature lacks discussion on textured hair care as a meaningful occupation. In the Canadian context, this topic remains unexplored. Purpose . The purpose of this study is to explore textured hair care as a meaningful occupation through experiences and perceptions of Canadian occupational therapists and occupational therapist assistants who identify as Black or Mixed race. Method . A qualitative thematic design was adopted; 11 occupational therapists and one occupational therapist assistant were interviewed. Interviews were transcribed and subsequently coded. Themes and subthemes were identified using thematic analysis. Seven participants engaged in a focus group to confirm preliminary findings. Findings . Five main themes were identified: textured hair is diverse, personal perceptions of textured hair, societal perceptions of textured hair, barriers to participating in textured hair care and addressing knowledge gaps in the profession. All themes are accompanied by subthemes. Conclusion . All participants acknowledged textured hair as a meaningful yet under-addressed occupation. This study begins a critical conversation based on lived experiences with textured hair to promote culturally safer research, education, and practice.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".