Trends and Shifts in Swedish Telemedicine Consultations During the Pre–COVID-19, COVID-19, and Post–COVID-19 Periods: Retrospective Observational Study
Bibliographic record
Abstract
Background: In recent times, the telemedicine landscape has changed dramatically; it serves as a bridge, connecting health care providers and patients, especially during challenges such as the recent COVID-19 pandemic. Objective: This study seeks to explore the Swedish telemedicine landscape in terms of primary patient symptoms for teleconsultation and the patterns of telemedicine use in the periods before COVID-19, during COVID-19, and after COVID-19, including the primary care use dynamics with respect to the teleconsultations done. Methods: Secondary data was used in this observational retrospective study. The study population consisted of Swedish residents who had online telemedicine consultations. Telemedicine consultations were divided by text and video delivery; the period of analysis ranged from November 2018 to June 2023. The statistical methods used for the data analysis were descriptive analysis, 2-way cross tabulation, and a generalized linear model. Results: During the pandemic, the number of teleconsultations concerning general, unspecified symptoms increased in comparison to the other analyzed symptoms, signaling a change in care-seeking behavior under epidemiological pressure. General health-related issues were the most pronounced symptom across all periods: 186.9 of 1000 consultations before COVID-19, 1264.6 of 1000 consultations during COVID-19, and 319.2 of 1000 consultations after COVID-19. There was no significant main effect of COVID-19 period on the number of telemedicine consultation meetings (F2=1.653; P=.38). The interaction effect between delivery type and period was statistically significant (F2=14.723; P<.001). Conclusions: The findings are in favor of the COVID-19 pandemic having had a considerable effect on telemedicine use. Telemedicine could subsequently be used more often for general health consultations and acute conditions. Video consultations were more prominent because of the importance of bidirectional communication. The study suggests that there was a transformation of patterns of demand for health care; there is a necessity for health care systems to respond to these changes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".