The art of body mapping: A methodological guide for social work researchers
Bibliographic record
Abstract
INTRODUCTION: The purpose of this Canadian social work research was to explore the healthcare experiences of men and women with a diagnosis of fibromyalgia (FM), a chronic condition of unknown origin.METHODS: This study had a total sample of 35 Southwestern Ontarians who participated in two separate qualitative methods of data collection. Ten participants completed in-depth interviews, while 25 participants engaged in body mapping, an arts-based research method, within a series of focus group sessions. The latter method for data collection is the focus of this article. This material provides social work researchers with a methodological road map by outlining the design and implementation of the body mapping process, sharing the lessons learned in data collection and addressing practical and ethical considerations for future studies.FINDINGS: This research found that: (a) participants experienced structural barriers to accessing healthcare services and unsupportive attitudes from healthcare providers; (b) participants’ healthcare experiences were affected by their gender, age, class and race; and (c) participants used self-management strategies to cope with healthcare barriers. The study also found that the body mapping process had therapeutic value.CONCLUSION: This research contributes information for the transformation of healthcare policies, programmes and clinical practices for the FM population. As a form of applied research, the body mapping process has also helped to empower a marginalised population while promoting innovative forms of social work research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".