Evidence map›Paper›PMID 35107794›Full record

ArticleJournal of cancer survivorship : research and practice2022

Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors.

Paul Harpur, Fitore Hyseni, Peter Blanck

Open access · bronzeAbstract read
In one paragraph

Article in Journal of cancer survivorship : research and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. On the Importance of the Americans with Disabilities Act at 30.Journal of disability policy studies · 2023
    Article
  2. Article
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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

3 authors at 3 institutions in 2 countries.

Paul HarpurTC Beirne School of Law, The University of Queensland, 1 University Drive, Brisbane, 4069, Australia.
Fitore HyseniMaxwell School of Citizenship and Public Affairs, Syracuse University, 950 Irving Avenue, Syracuse, NY, 13244, USA.
Peter BlanckBurton Blatt Institute, Syracuse University, 950 Irving Avenue, Suite 446, Syracuse, NY, 13244, USA. pblanck@syr.edu.ORCID 0000-0001-9480-1527
Harvard University Press · USSyracuse University · USThe University of Queensland · AU

Funding

ACL HHS 90DP0090ACL HHS 90DPAD0005ACL HHS 90RTEM0006NCCDPHP CDC HHS K01 DP000090
6 · The paper itself

Abstract

purposeThis article examines ways COVID-19 health surveillance and algorithmic decision-making ("ADM") are creating and exacerbating workplace inequalities that impact post-treatment cancer survivors. Cancer survivors' ability to exercise their right to work often is limited by prejudice and health concerns. While cancer survivors can ostensibly elect not to disclose to their employers when they are receiving treatments or if they have a history of treatment, the use of ADM increases the chances that employers will learn of their situation regardless of their preferences. Moreover, absent significant change, inequalities may persist or even expand.

methodsWe analyze how COVID-19 health surveillance is creating an unprecedented amount of health data on all people. These data are increasingly collected and used by employers as part of COVID-19 regulatory interventions.

resultsThe increase in data, combined with the health and economic crisis, means algorithm-driven health inequalities will be experienced by a larger percentage of the population. Post-treatment cancer survivors, as for people with disabilities generally, are at greater risk of experiencing negative outcomes from algorithmic health discrimination.

conclusionsUpdated and revised workplace policy and practice requirements, as well as collaboration across impacted groups, are critical in helping to control the inequalities that flow from the interaction between COVID-19, ADM, and the experience of cancer survivorship in the workplace. IMPLICATIONS FOR CANCER SURVIVORS: The interaction among COVID-19, health surveillance, and ADM increases exposure to algorithmic health discrimination in the workplace.

Indexed as

Cancer SurvivorsCOVID-19NeoplasmsHumansPrejudiceSARS-CoV-2WorkplaceAlgorithmic health discriminationCancerChronic illnessCOVID-19DisabilityHealth surveillance

Identifiers

PMID35107794
PMCPMC8809228
OpenAlexW4210440322

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.