Discovering a “sense of community”: patient experiences of connection in intentionally remote eating disorder care
Bibliographic record
Abstract
While some findings indicate high levels of patient satisfaction with remote eating disorder treatment, others reflect feelings of disconnection due to unique telehealth treatment challenges. The COVID-19 pandemic presented circumstances that likely impacted the findings established thus far. As such, the present study sought to understand patient experiences of connection in an intentionally remote eating disorder treatment program, specifically in a context outside of pandemic transition. A secondary analysis of de-identified qualitative data previously obtained for quality improvement purposes via a client satisfaction survey was conducted. Adult patient responses (N = 38) were analyzed via reflexive thematic analysis within a critical realist framework. Four themes were generated from the data: (1) Embracing one’s humanity paves the way for connection, (2) Discovering a “sense of community” in remote care, (3) “They made me feel I was worth recovering”: connection as a vehicle for healing, and (4) Aligning expectations and reality: reconceptualizing the journey to connection in remote eating disorder care. Overall, findings suggest that it is possible for patients to form strong, impactful connections in remote treatment. Importantly, patient perspectives indicated that there were shifts in how these connections were experienced as a result of the remote care environment (e.g., how support could be provided and by whom). Considerations unique to remote care (e.g., offering training to improve clients’ comfort with technology) may be important to fostering connection, thereby contributing to improved patient experiences and treatment outcomes. This study investigated patient experiences of connection in remote eating disorder treatment using satisfaction survey data. Patients reported forming strong interpersonal connections that were deeply impactful to their recovery, emphasizing the importance of feeling understood, accepted, and cared for in building these connections. However, they noted some differences in how these connections were experienced as compared to in-person settings (e.g., how support could be provided and by whom), indicating a need for adaptations in remote care (e.g., training on how to use technology). These lived experience perspectives can help to inform the ongoing implementation of remote eating disorder treatment to improve patient care.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".