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Record W2991395463 · doi:10.2196/15564

Exploring Mental Health Professionals’ Perspectives of Text-Based Online Counseling Effectiveness With Young People: Mixed Methods Pilot Study

2019· article· en· W2991395463 on OpenAlexvenueno aff
Pablo Navarro, Jeanie Sheffield, Sisira Edirippulige, Matthew Bambling

Bibliographic record

VenueJMIR Mental Health · 2019
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthMedical educationPsychologyHealth professionalsMedicineApplied psychologyPsychotherapistHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Population-based studies show that the risk of mental ill health is highest among young people aged 10 to 24 years, who are also the least likely to seek professional treatment because of a number of barriers. Electronic mental (e-mental) health services have been advocated as a method for decreasing these barriers for young people, among which text-based online counseling (TBOC) is a primary intervention used at many youth-oriented services. Although TBOC has shown promising results, its outcome variance is greater in comparison with other electronic interventions and adult user groups. OBJECTIVE: This pilot study aimed to explore and confirm e-mental health professional's perspectives about various domains and themes related to young service users' (YSUs) motivations for accessing TBOC services and factors related to higher and lower effectiveness on these modalities. METHODS: Participants were 9 e-mental health professionals who were interviewed individually and in focus groups using a semistructured interview. Thematic analysis of qualitative themes from interview transcripts was examined across the areas of YSU motivations for access and factors that increase and decrease TBOC effectiveness. RESULTS: A total of 4 domains and various subthemes were confirmed and identified to be related to YSUs' characteristics, motivations for accessing TBOC, and moderators of service effectiveness: user characteristics (ie, prior negative help-seeking experience, mental health syndrome, limited social support, and perceived social difficulties), selection factors (ie, safety, avoidance motivation, accessibility, and expectation), and factors perceived to increase effectiveness (ie, general therapeutic benefits, positive service-modality factors, and persisting with counseling despite substantial benefit) and decrease effectiveness (ie, negative service-modality factors). CONCLUSIONS: Participants perceived YSUs to have polarized expectations of TBOC effectiveness and be motivated by service accessibility and safety, in response to several help-seeking concerns. Factors increasing TBOC effectiveness were using text-based communication, the online counselor's interpersonal skills and use of self-management and crisis-support strategies, and working with less complex presenting problems or facilitating access to more intensive support. Factors decreasing TBOC effectiveness were working with more complex problems owing to challenges with assessment, the slow pace of text communication, lack of nonverbal conversational cues, and environmental and connectivity issues. Other factors were using ineffective techniques (eg, poor goal setting, focusing, and postcounseling direction) that produced only short-term outcomes, poor timeliness in responding to service requests, rupture in rapport from managing service boundaries, and low YSU readiness and motivation.

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)0.016
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 score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.094
GPT teacher head0.485
Teacher spread0.391 · 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".

Quick stats

Citations43
Published2019
Admission routes1
Has abstractyes

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