Evidence map›Paper›PMID 25535280›Full record

ArticleFamily practice2015

Clinic staff attitudes towards the use of mHealth technology to conduct perinatal depression screenings: a qualitative study.

Maria Pineros-Leano, Karen M Tabb, Heather Sears, Brandon Meline, Hsiang Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Family practice, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 5 pooled it
–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

13 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Article
  8. Article
  9. Article
  10. AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020
    Article
  11. Review
  12. Mobile Based mhGAP-IG Depression Screening in Kenya.Community mental health journal · 2018
    Article
  13. 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.

Maria Pineros-LeanoSchool of Social Work, University of Illinois at Urbana-Champaign, Urbana, IL, USA, Identifying Depression through Early Assessment (IDEA) Research Team, Urbana, IL, USA, pineros1@illinois.edu.
Karen M TabbSchool of Social Work, University of Illinois at Urbana-Champaign, Urbana, IL, USA, Identifying Depression through Early Assessment (IDEA) Research Team, Urbana, IL, USA.
Heather SearsSchool of Social Work, University of Illinois at Urbana-Champaign, Urbana, IL, USA, Identifying Depression through Early Assessment (IDEA) Research Team, Urbana, IL, USA.
Brandon MelineIdentifying Depression through Early Assessment (IDEA) Research Team, Urbana, IL, USA, Champaign Urbana Public Health District, Champaign, IL, USA.
Hsiang HuangIdentifying Depression through Early Assessment (IDEA) Research Team, Urbana, IL, USA, Department of Psychiatry, Cambridge Health Alliance, Harvard Medical School, Cambridge, MA, USA and Institute of Psychiatry & LIM-23, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.

Funding

NIMHD NIH HHS L60 MD008481
6 · The paper itself

Abstract

backgroundThe use of mHealth technology is an innovative approach for screening low-income mothers for depression. Past studies show that the use of technology removes barriers such as literacy issues, language challenges, concerns about privacy and lack of transportation and can also increase reliability. However, little is known about staff attitudes and perceptions towards using mHealth technology for screening low-income women for depression in clinics.

methodsFour focus groups were conducted with staff members in a supplemental nutrition program for women, infants and children located in a public health clinic. A semi-structured focus group interview guide was used to examine staff perceptions related to depression screening with tablet technology. All interviews were audio recorded and transcribed verbatim. Thematic analysis was used to analyse all focus group data.

resultsThree major benefits and two major barriers were found. The benefits of using technology for perinatal depression screenings were reduction of literacy and language barriers, reduction of redundancy and errors and increased privacy for clients. The barriers were increased network issues and responsibility for technology, which included fear of the devices being lost, stolen or broken. IMPLICATIONS: Before implementing mHealth tablet technology for depression screening in a public health clinic, it is important to address the concerns of staff members to make the transition more effective. This study provides timely information on staff-perceived benefits and barriers when implementing mHealth technology in a public health setting.

Indexed as

Attitude of Health PersonnelComputers, HandheldAmbulatory Care FacilitiesCommunication BarriersDepressionFemaleFocus GroupsHumansLiteracyLocal Area NetworksLocal GovernmentMass ScreeningPostpartum PeriodPovertyPrivacyQualitative ResearchDepression screeningmHealth technologyperinatalpublic health clinicWIC.

Identifiers

PMID25535280
PMCPMC7340322

What OpenQuestion holds

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LicenceCC BY-NC
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.