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Record W2607736873 · doi:10.2196/mental.7231

Gathering Opinions on Depression Information Needs and Preferences: Samples and Opinions in Clinic Versus Web-Based Surveys

2017· article· en· W2607736873 on OpenAlexafffundvenueabout
Matthew T. Bernstein, John R. Walker, Kathryn A. Sexton, Alan Katz, Brooke Beatie

Bibliographic record

VenueJMIR Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsManitoba HealthUniversity of Manitoba
FundersFogarty International CenterCanadian Institutes of Health ResearchMental Health Commission
KeywordsFamily medicineDepression (economics)MedicineSample (material)NoticePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: There has been limited research on the information needs and preferences of the public concerning treatment for depression. Very little research is available comparing samples and opinions when recruitment for surveys is done over the Web as opposed to a personal invitation to complete a paper survey. OBJECTIVE: This study aimed to (1) to explore information needs and preferences among members of the public and (2) compare Clinic and Web samples on sample characteristics and survey findings. METHODS: Web survey participants were recruited with a notice on three self-help association websites (N=280). Clinic survey participants were recruited by a research assistant in the waiting rooms of a family medicine clinic and a walk-in medical clinic (N=238) and completed a paper version of the survey. RESULTS: The Clinic and Web samples were similar in age (39.0 years, SD 13.9 vs 40.2 years, SD 12.5, respectively), education, and proportion in full time employment. The Clinic sample was more diverse in demographic characteristics and closer to the demographic characteristics of the region (Winnipeg, Canada) with a higher proportion of males (102/238 [42.9%] vs 45/280 [16.1%]) and nonwhites (Aboriginal, Asian, and black) (69/238 [29.0%] vs 39/280 [13.9%]). The Web sample reported a higher level of emotional distress and had more previous psychological (224/280 [80.0%] vs 83/238 [34.9%]) and pharmacological (202/280 [72.1%] vs 57/238 [23.9%]) treatment. In terms of opinions, most respondents in both settings saw information on a wide range of topics around depression treatment as very important including information about treatment choices, effectiveness of treatment, how long it takes treatment to work, how long treatment continues, what happens when treatment stops, advantages and disadvantages of treatments, and potential side effects. Females, respondents with a white background, and those who had received or felt they would have benefited from therapy in the past saw more information topics as very important. Those who had received or thought they would have benefited in the past from medication treatment saw fewer topics as important. Participants in both groups expressed an interest in receiving information through discussion with a counselor or a physician, through written brochures, or through a recommended website. CONCLUSIONS: The recruitment strategies were helpful in obtaining opinions from members of the public with different concerns and perspectives, and the results from the two methods were complementary. Persons coping with emotional distress and individuals not specifically seeking help for depression would be interested in information to answer a wide range of important questions about depression treatment. The Clinic sample yielded more cultural diversity that is a closer match to the population. The Web sample was less costly to recruit and included persons who were most interested in receiving information.

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)0.040
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.131
GPT teacher head0.451
Teacher spread0.320 · 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
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

Citations9
Published2017
Admission routes4
Has abstractyes

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