The Impact of COVID-19 on Complementary and Alternative Medicine Providers: A Cross-sectional Survey in Norway
Bibliographic record
Abstract
<title>Abstract</title> BackgroundThe Norwegian authorities decided on a nationwide lockdown to prevent spread of the COVID-19 virus. The lockdown had huge socioeconomic consequences for the society. The aim of this study was to investigate the impact of COVID-19 on Complementary and Alternative Medicine (CAM) providers in Norway.MethodThis cross-sectional survey analyzed data from a self-administrated questionnaire. A total of 581 CAM providers completed the questionnaire, which was designed to describe consequences for CAM providers and their clinical practice after the nationwide lockdown. Between group differences were analyzed using chi-square, ANOVA and Fisher’s exact test. Significance level was defined as p < 0.05 without adjustment for multiple comparisons.ResultDuring the nationwide lockdown of Norway, 38.4% of respondents were able to provide CAM treatment to their patients. Of those, the majority (96.4%) had reorganized their clinical practice in accordance with COVID-19 hygiene regulations, offered video consultations (57.4%) or telephone consultations (46.6%). To manage financially during the lockdown, half of the providers spent their savings (48.7%). More than one third (35.1%) was supported by their partner, and 26.7% received compensation from the Norwegian state. A total of 26.3% of the CAM providers had other paid work that provided them with income. Nearly a quarter (18.6%) borrowed money from friends and family, changed their loan terms, or took out new bank loans. The majority (62.7%) expressed uncertainty about the future of their practice. CAM providers who had reorganized their practice to online consultations were more optimistic. ConclusionThe impact of COVID-19 on CAM providers was considerable. It adversely affected their clinical practice, financial situation, and view on their future practice. To ensure that the health needs of the Norwegian population regarding CAM use are met during pandemic times like COVID-19, it is recommended to support and train CAM providers in the development of online CAM services, as well as efficient implementation of infection prevention and control measures.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".