Exploring the Telehealth Experiences of Service Users with Mental-Physical Multimorbidity during the COVID-19 pandemic
Bibliographic record
Abstract
Context The COVID-19 pandemic led to significant transformations in health care delivery, notably in the shift towards telehealth. While previous studies have shed light on the benefits and challenges of the rapid shift to telehealth, few studies have explored the telehealth experiences of service users with coexisting mental and physical health conditions (i.e. mental-physical multimorbidity). Objective To explore the telehealth experiences of adults with mental-physical multimorbidity during the COVID-19 pandemic. Study design, population, data collection and analyses We conducted a qualitative descriptive study involving 29 semi-structured interviews with adults living with mental-physical multimorbidity. Participants were asked about their experiences of care during the pandemic, including their positive and negative telehealth experiences. How telehealth influenced the accessibility, continuity, person-centredness and comprehensiveness was explored. Interview transcripts were analyzed using a mixed inductive-deductive thematic analysis approach to identify recurrent themes in participants’ experiences. Analyses were supported by NVivo software and themes were discussed by an interdisciplinary team. Setting Quebec, Canada Results Findings suggest that most participants had both positive and negative care experiences with care delivered through telehealth. While telehealth maintained or improved access to certain types of care such as psychological or routine physical care, access to appropriate care for some physical health conditions remained limited. Telehealth also appeared to help maintain relational continuity between services users and their care providers throughout the pandemic. However, many participants shared negative experiences related to the person-centredness of care delivered via telehealth and fully comprehensive care was often difficult to achieve. Conclusions These findings shed light on areas that can be targeted for quality improvement to improve the telehealth experiences of individuals with mental-physical multimorbidity.
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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 | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".