Web-Based Consumer Health Education About Back Pain: Findings of Potential Tensions From a Photo-Elicitation and Observational Study
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is a leading cause of disability worldwide, with huge social and economic impact. There is extensive extant literature investigating the efficacy of various management approaches ranging from surgery to psychological interventions to exercise. However, this work has focused almost entirely on efficacy in terms of pain reduction, functional improvement, and psychological changes. This focus has meant that unanticipated social or socio-cultural effects of back pain health care have received little attention. OBJECTIVE: This study aimed to scrutinize some of the conceptual tensions inherent in contemporary LBP health care approaches and to highlight their material effects. METHODS: We used a qualitative research design adapted from discourse analysis, which was able to consider key discursive tensions underpinning a LBP website. Data collection involved observing the interaction between adult participants with LBP and the website in the following two ways: (1) observational interview, where participants were observed interacting with the website for the first time and asked to discuss their responses to it as they moved through the website and (2) photo-elicitation, where for a month after their first use of the website, people took photographs of what was happening in their lives when they thought of the website and discussed them in a follow-up interview. We used a postcritical discourse analysis approach to examine data produced from these methods. RESULTS: Our postcritical discourse analysis identified key discursive tensions, including between living with and reducing LBP, keeping active and resting, and patient choice and giving guidance. CONCLUSIONS: Our analysis suggests ways for considering less dominant perspectives without having to discard the benefits of dominant ones. Although the focus of LBP discourses has changed (less biomedical and less about cure), they still hold on to some of the problematic dominant paradigmatic concepts such as biomedicine and individualism. The tensions we highlight are likely to be highly useful for teaching and implementing LBP care across multiple health care settings.
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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 | 0.043 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".