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Record W4413407498 · doi:10.2196/67949

Exploring the Characteristics of Online Counseling Chat Services for Youth in Europe: Web Search Study

2025· review· en· W4413407498 on OpenAlexvenueno aff
Irati Higuera-Lozano, Virvatuli Uusimäki, Tuuli Pitkänen, Elke Denayer, Alexis Dewaele, Katalin Felvinczi, Lien Goossens, Zsuzsa Kaló, Mónika Rényi, María Cabello

Bibliographic record

VenueJMIR Mental Health · 2025
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersErasmus+European CommissionStrategic Research CouncilComunidad de Madrid
KeywordsWorld Wide WebInternet privacyThe InternetPsychologyMedical educationComputer scienceMultimediaMedicine

Abstract

fetched live from OpenAlex

Background: Online counseling chat services are increasingly used by young people worldwide. A growing body of literature supports the use and effectiveness of these services for adolescent mental health. However, there is also a need to provide an overview of the main existing resources to identify unmet needs and gaps in the field. Objective: This study aims to provide an overview of existing online counseling chat services targeting individuals aged 12-30 years in 4 European countries (Belgium, Finland, Hungary, and Spain), and to identify potential needs and gaps by comparing the collected data with recognized quality standard criteria that define the best practices in the field of counseling. Methods: A web search was conducted in the 4 participating countries using the same keywords to identify the main chat services. The final selection of chat services was made using a stratified purposive sampling method. A common data extraction database was developed to record information from these websites. Finally, the extracted information was compared against the fulfillment of 7 selected criteria from the Child Helpline International Quality Standards Framework. Additionally, certain chat characteristics were compared with the number of Child Helpline Quality Standard criteria fulfilled. Results: The search identified a total of 66 service providers offering 71 different chat services. Nongovernmental organizations accounted for more than half of the total service providers42 of 66 (64%). Additional helplines, such as hotlines, were also available through 54 of 66 (82%) service providers. Artificial intelligence tools were incorporated into 6 of 66 (9%) chat services. Differences were observed between countries; for example, the use of volunteers as counselors was predominant in Hungary and Belgium. Topic-specific chat services were common in Belgium and Spain, whereas in Finland and Hungary, chat services generally welcomed a wide range of topics for young people to discuss. Comparisons with Child Helpline International's recommendations revealed some gaps-for example, only 9 of 71 (13%) chat services operated 24 hours a day, and only 10 of 71 (14%) offered interactions in minority groups or foreign languages. Additionally, the use of free social media platforms for chat services was prevalent in some countries, which could compromise users' privacy. Being part of the Child Helpline International consortium was marginally associated with meeting a higher number of standard criteria (β coefficient 1.55; P=.08). Conclusions: This study provides a comprehensive overview of existing online chat counseling services in 4 European countries. Our findings suggest that some existing chat services for young people could be improved in areas such as accessibility, data security, and the inclusion of vulnerable groups.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.011
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.258
GPT teacher head0.511
Teacher spread0.254 · 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 designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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