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Record W3003162347 · doi:10.2196/16817

Portuguese Psychologists' Attitudes Toward Internet Interventions: Exploratory Cross-Sectional Study

2020· article· en· W3003162347 on OpenAlexvenueno aff
Cristina Mendes-Santos, Elisabete Weiderpass, Rui Santana, Gerhard Andersson

Bibliographic record

VenueJMIR Mental Health · 2020
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersErasmus+European CommissionWorld Health Organization
KeywordsPsychological interventionPortugueseThe InternetMedicineComputer-assisted web interviewingFamily medicinePsychologyMedical educationApplied psychologyNursingWorld Wide WebBusinessComputer science

Abstract

fetched live from OpenAlex

Background Despite the significant body of evidence on the efficacy and cost-effectiveness of internet interventions, the implementation of such programs in Portugal is virtually non-existent. In addition, Portuguese psychologists’ use and their attitudes towards such interventions is largely unknown. Objective The aim of this study was to explore Portuguese psychologists’ knowledge, training, use and attitudes towards internet interventions; to investigate perceived advantages and limitations of such interventions; identify potential drivers and barriers impacting implementation; and study potential factors associated to previous use and attitudes towards internet interventions. Methods An online cross-sectional survey was developed by the authors and disseminated by the Portuguese Psychologists Association to its members. Results A total of 1077 members of the Portuguese Psychologists Association responded to the questionnaire between November 2018 and February 2019. Of these, 37.2% (N=363) were familiar with internet interventions and 19.2% (N=188) considered having the necessary training to work within the field. 29.6% (N=319) of participants reported to have used some form of digital technology to deliver care in the past. Telephone (23.8%; N=256), e-mail (16.2%; N=175) and SMS (16.1%; N=173) services were among the most adopted forms of digital technology, while guided (1.3%; N=14) and unguided (1.5%; N=16) internet interventions were rarely used. Accessibility (79.9%; N=860), convenience (45.7%; N=492) and cost-effectiveness (45.5%; N=490) were considered the most important advantages of internet interventions. Conversely, ethical concerns (40.7%; N=438), client’s ICT illiteracy (43.2%; N=465) and negative attitudes towards internet interventions (37%; N=398) were identified as the main limitations. An assessment of participants attitudes towards internet interventions revealed a slightly negative/neutral stance (Median=46.21; SD=15.06) and revealed greater acceptability towards blended treatment interventions (62.9%; N=615) when compared to standalone internet interventions (18.6%; N=181). Significant associations were found between knowledge (χ24=90.4; P<.001), training (χ24=94.6; P<.001), attitudes (χ23=38.4; P<.001) and previous use of internet interventions and between knowledge (χ212=109.7; P<.001), training (χ212=64.7; P<.001) and attitudes towards such interventions, with psychologists reporting to be ignorant and not having adequate training in the field, being more likely to present more negative attitudes towards these interventions and not having prior experience in its implementation. Conclusions This study revealed that most Portuguese psychologists are not familiar with and have no training or prior experience using internet interventions and had a slightly negative/neutral attitude towards such interventions. There was greater acceptability towards blended treatment interventions compared to standalone internet interventions. Lack of knowledge and training were identified as the main barriers to overcome, underlining the need of promoting awareness and training initiatives to ensure internet interventions successful implementation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.207
GPT teacher head0.512
Teacher spread0.305 · 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; both teacher heads agree on what is shown here.

Study designObservational
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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Citations56
Published2020
Admission routes1
Has abstractyes

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