Exploring Rehabilitation Provider Experiences of Providing Health Services for People Living with Long COVID in Alberta
Bibliographic record
Abstract
BACKGROUND: COVID-19 infection can result in persistent symptoms, known as long COVID. Understanding the provider experience of service provision for people with long COVID symptoms is crucial for improving care quality and addressing potential challenges. Currently, there is limited knowledge about the provider experience of long COVID service delivery. AIM: To explore the provider experience of delivering health services to people living with long COVID at select primary, rehabilitation, and specialty care sites. DESIGN AND SETTING: This study employed qualitative description methodology. Semi-structured interviews were conducted with frontline providers at primary care, rehabilitation, and specialty care sites across Alberta. Participants were interviewed between June and September 2022. METHOD: Interviews were conducted virtually over zoom, audio-recorded, and transcribed with consent. Iterative inductive qualitative content analysis of transcripts was employed. Relationships between emergent themes were examined for causality or reciprocity, then clustered into content areas and further abstracted into a priori categories through their interpretive joint meaning. PARTICIPANTS: A total of 15 participants across Alberta representing diverse health care disciplines were interviewed. RESULTS: Main themes include: the importance of education for long COVID recognition; the role of symptom acknowledgement in patient-centred long COVID service delivery; the need to develop recovery expectations; and opportunities for improvement of navigation and wayfinding to long COVID services. CONCLUSIONS: Provider experience of delivering long COVID care can be used to inform patient-centred service delivery for persons with long COVID symptoms.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".