Evidence map›Paper›PMID 34248317›Full record

ArticleEthics & behavior2021

Considerations for the Ethical Implementation of Psychological Assessment Through Social Media via Machine Learning.

Megan N Fleming

Open access · greenAbstract read
In one paragraph

Article in Ethics & behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 22% 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, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

1 author at 1 institution in 1 country.

Megan N FlemingDepartment of Psychological Sciences, University of Missouri - Columbia.
University of Missouri · US

Funding

Psychology of Alcohol Use and Dependence TrainingT32AA013526 · NIAAA · UNIVERSITY OF MISSOURI-COLUMBIA · PI DENIS M MCCARTHY, KENNETH JAMES SHER · 2002 to 2026
$9.0M
NIAAA NIH HHS T32 AA013526
6 · The paper itself

Abstract

The ubiquity of social media usage has led to exciting new technologies such as machine learning. Machine learning is poised to change many fields of health, including psychology. The wealth of information provided by each social media user in combination with machine learning technologies may pave the way for automated psychological assessment and diagnosis. Assessment of individuals' social media profiles using machine learning technologies for diagnosis and screening confers many benefits (i.e., time and cost efficiency, reduced recall bias, information about an individual's emotions and functioning spanning months or years, etc.); however the implementation of these technologies will pose unique challenges to the professional ethics of psychology. Namely, psychologists must understand the impact of these assessment technologies on privacy and confidentiality, informed consent, recordkeeping, bases for assessments, and diversity and justice. This paper offers a brief review of the current applications of machine learning technologies in psychology and public health, provides an overview of potential implementations in clinical settings, and introduces ethical considerations for professional psychologists. This paper presents considerations which may aid in the extension of the current Ethical Principles of Psychologists and Code of Conduct to address these important technological advancements in the field of clinical psychology.

Indexed as

automated assessmentEthical Principles of Psychologistsmachine learning in psychologypsychologysocial media

Identifiers

PMID34248317
PMCPMC8261642
OpenAlexW3084938715

What OpenQuestion holds

Textmetadata
LicenceTDM
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.