Evidence map›Paper›PMID 35689752›Full record

ArticleThe Psychiatric quarterly2022

The Good, The Bad, The Uncertain: Diverse Provider Experiences with Telemental Health During COVID-19.

Jessica L Lawson, Jennifer M Doran, McKenna O'Shea, Erica A Abel

Open access · bronzeAbstract read
In one paragraph

Article in The Psychiatric quarterly, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
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

4 citing papers in PubMed, 9 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Jessica L LawsonVA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, USA. jessica.lawson1@va.gov.ORCID 0000-0001-5508-4262
Jennifer M DoranVA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, USA.
McKenna O'SheaRoosevelt University, 430 S. Michigan Avenue, Chicago, IL, USA.
Erica A AbelVA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, USA.
Yale University · USRoosevelt University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study assessed mental health provider attitudes and perceptions of telemental health (TMH) prior to and during the COVID-19 Pandemic. The study expands on earlier work by providing a more detailed qualitative analysis of provider perceptions of TMH, including its efficacy, advantages, and limitations. The current study is part of a larger mixed methods project utilizing a repeated cross-sectional design. An online survey was administered to a sample of 1,448 mental health providers. Of the survey participants, 934 offered narrative responses to open-ended questions and were included in the present study. Qualitative data was analyzed using a coding team and the Consensual Qualitative Research paradigm. Providers described both positive and negative feelings about using TMH during the COVID-19 Pandemic. Several advantages were identified, with providers clearly appreciating the role of TMH in allowing them to work continuously and safely during the public health emergency. An array of negative views and concerns were also expressed, including that TMH may not be optimal or effective in certain settings or situations. A portion of respondents also indicated a preference for face-to-face care and illuminated ways they found TMH lacking or limited.

Indexed as

COVID-19Mental Health ServicesTelemedicineCross-Sectional StudiesHumansPandemicsAttitudesCOVID-19 pandemicEffectivenessProvider perceptionsTelemental health

Identifiers

PMID35689752
PMCPMC9187881
OpenAlexW4282913762

What OpenQuestion holds

Textmetadata
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.