Practical and Emotional Problems Reported by Users of a Self-guided Digital Problem-solving Intervention During the COVID-19 Pandemic: Content Analysis
Bibliographic record
Abstract
BACKGROUND: To better direct assessments and interventions toward the general population during both the ongoing COVID-19 pandemic and future crises with societal restrictions, data on the types of practical and emotional problems that people are experiencing are needed. OBJECTIVE: The aim of this study was to examine the types of practical and emotional problems that the general population is experiencing during the COVID-19 pandemic and to construct an empirically derived inventory based on the findings. METHODS: A total of 396 participants, recruited among members of the general public in Sweden who were experiencing practical and/or emotional problems during the pandemic, accessed a self-guided digital problem-solving intervention for a period of 1 week to report and solve the problems they experienced. Prior to accessing the intervention, the participants completed a short self-assessment regarding symptoms of depression and anxiety. Content analysis was used to account for the types of problems participants reported. A set of items for an inventory was later proposed based on the problem categories derived from the analysis. RESULTS: A majority of participants had clinically relevant symptoms of either depression or anxiety. The problems reported were categorized as 13 distinct types of problems. The most common problem was difficulty managing daily activities. Based on the categories, a 13-item inventory was proposed. CONCLUSIONS: The 13 types of problems, and the proposed inventory, could be valuable when composing assessments and interventions for the general population during the ongoing pandemic or similar crises with societal restrictions. The most common problem was of a practical nature, indicating the importance of including examples of such problems within assessments and interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04677270; https://clinicaltrials.gov/ct2/show/NCT04677270.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".