Health Care Professionals’ Experiences in Telerehabilitation: Qualitative Content Analysis
Bibliographic record
Abstract
BACKGROUND: The use of digital communication in Swedish health care has increased in an effort to make health care more accessible. At the organizational level, trust in digitalization has stabilized, but a certain degree of skepticism regarding technology appears to exist among health care employees. OBJECTIVE: This study aimed to explore health care professionals' (HCPs) experiences of digital communication with patients and colleagues in a habilitation context. METHODS: Qualitative content analysis was used to analyze data derived from individual interviews. RESULTS: The results revealed that there were mixed feelings regarding the digital format used at the habilitation center. Although some skepticism remained regarding the digital format, there seemed to be a parallel understanding of the motives and benefits of digitalization. Hence, positive aspects, such as increased health care accessibility, were identified. However, emphasis was placed on the considerations required to make digital consultations appropriate for each patient. CONCLUSIONS: Managing a workday influenced by the balance between digital and physical demands forces HCPs to adjust to the digital format and new ways of working. This requires HCPs to consider whether digital means are appropriate for communication in individual patient-specific cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".