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Record W4411202722 · doi:10.2196/66908

Experiences of Individually Tailored Internet-Based Cognitive Behavioral Therapy During the COVID-19 Pandemic: Qualitative Interview Study

2025· article· en· W4411202722 on OpenAlexvenueno aff
Victoria Aminoff, Matilda Baltius, Matilda Berg, Gerhard Andersson, Mikael Ludvigsson

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Qualitative researchThe InternetPsychology2019-20 coronavirus outbreakCognitive behavioral therapyCognitionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychotherapistMedicinePsychiatryVirologySociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: During the COVID-19 pandemic, both physical and psychological health were at risk. Internet-based cognitive behavioral therapy (ICBT) is a psychological treatment alternative that does not inherently increase the risk of virus transmission because face-to-face interactions are not required. ICBT has been found to be effective for a variety of mental health problems, both before and during the COVID-19 pandemic. Although the experiences of undergoing ICBT have been investigated in previous studies, the specific experiences of participating in ICBT during the COVID-19 pandemic have been less examined. OBJECTIVE: This qualitative study aimed to investigate the experiences of participants undergoing individually tailored ICBT with weekly therapist support during the COVID-19 pandemic. METHODS: We approached trial participants who had received ICBT for psychological symptoms related to the COVID-19 pandemic during the summer of 2020. A strategic sample, based on the number of log-ins to the treatment platform, among other factors, was selected in an effort to achieve the highest possible variation. Semistructured telephone interviews were conducted 4 to 6 months after treatment completion, depending on whether the participant was initially assigned to the treatment or control group. Data were transcribed and then analyzed based on thematic analysis. RESULTS: A total of 16 participants aged between 23 and 78 years were interviewed. Four main themes and 10 subthemes were derived from the thematic analysis: (1) functions of the treatment (initiating and motivating, perspective widening), (2) treatment equals work (experience of the treatment as demanding, going from text to action, posttreatment engagement, participant agency), (3) changes experienced (changes in relation to the COVID-19 pandemic, other changes not related to the COVID-19 pandemic), and (4) wishing for something else (individually tailored, contact with the therapist). CONCLUSIONS: The results closely align with those of previous qualitative studies on experiences of ICBT. Participants expressed appreciation of the treatment's content and format. Suggestions and wishes for changes were also expressed in the interviews. However, a unique finding was that participants described experiencing changes in well-being related to the COVID-19 pandemic. At the same time, there were also reports of changes in other symptoms not related to the pandemic. Further studies are needed on the experiences of participants who drop out of ICBT and the type of therapist contact they prefer.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.009
Scholarly communication0.0030.004
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.404
GPT teacher head0.628
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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