Evidence map›Paper›PMID 29426812›Full record

ArticleJournal of medical Internet research2018

Toward Impactful Collaborations on Computing and Mental Health.

Rafael Alejandro Calvo, Karthik Dinakar, Rosalind Picard, Helen Christensen, John Torous

Abstract readEditorial
In one paragraph

Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Toward Impactful Collaborations on Computing and Mental Health.Journal of medical Internet research · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rafael Alejandro Calvo *Wellbeing Supportive Technology Lab, School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.ORCID 0000-0003-2238-0684
Karthik DinakarMassachusetts Institute of Technology Media Lab, Cambridge, MA, United States.ORCID 0000-0001-7482-0237
Rosalind PicardMassachusetts Institute of Technology Media Lab, Cambridge, MA, United States.ORCID 0000-0002-5661-0022
Helen ChristensenBlack Dog Institute, Sydney, Australia.ORCID 0000-0003-0435-2065
John TorousDivision of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-5362-7937

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

We describe an initiative to bring mental health researchers, computer scientists, human-computer interaction researchers, and other communities together to address the challenges of the global mental ill health epidemic. Two face-to-face events and one special issue of the Journal of Medical Internet Research were organized. The works presented in these events and publication reflect key state-of-the-art research in this interdisciplinary collaboration. We summarize the special issue articles and contextualize them to present a picture of the most recent research. In addition, we describe a series of collaborative activities held during the second symposium and where the community identified 5 challenges and their possible solutions.

Indexed as

Biomedical ResearchHumansInterdisciplinary PlacementMental Healthdigital interventionshuman-computer interactioninterdisciplinary collaborationmental health

Identifiers

PMID29426812
PMCPMC5889813

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.