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Chronic Health Conditions and Longitudinal Employment in Survivors of Childhood Cancer

2024· article· en· W4396814139 on OpenAlexaff
Neel S. Bhatt, Pamela J. Goodman, Wendy M. Leisenring, Gregory T. Armstrong, Eric J. Chow, Melissa M. Hudson, Kevin R. Krull, Paul C. Nathan, Kevin C. Oeffinger, Leslie L. Robison, Anne C. Kirchhoff, Daniel A. Mulrooney

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Cancer Institute
KeywordsChildhood cancerLongitudinal dataLongitudinal studyQuality of life (healthcare)GerontologyPsychologyMedicineCancerDemographySociologyNursing

Abstract

fetched live from OpenAlex

Importance: Employment is an important factor in quality of life and provides social and economic support. Longitudinal data on employment and associations with chronic health conditions for adult survivors of childhood cancer are lacking. Objective: To evaluate longitudinal trends in employment among survivors of childhood cancer. Design, Setting, and Participants: Retrospective cohort study of 5-year cancer survivors diagnosed at age 20 years or younger between 1970 and 1986 enrolled in the multi-institutional Childhood Cancer Survivor Study (CCSS). Sex-stratified employment status at baseline (2002 to 2004) and follow-up (2014 to 2016) was compared with general population rates from the Behavioral Risk Factor Surveillance System cohort. Data were analyzed from July 2021 to June 2022. Exposures: Cancer therapy and preexisting and newly developed chronic health conditions. Main Outcomes and Measures: Standardized prevalence ratios of employment (full-time or part-time, health-related unemployment, unemployed, not in labor force) among adult (aged ≥25 years) survivors between baseline and follow-up compared with the general population. Longitudinal assessment of negative employment transitions (full-time to part-time or unemployed at follow-up). Results: Female participants (3076 participants at baseline; 2852 at follow-up) were a median (range) age of 33 (25-53) years at baseline and 42 (27-65) years at follow-up; male participants (3196 participants at baseline; 2557 at follow-up) were 33 (25-54) and 43 (28-64) years, respectively. The prevalence of full-time or part-time employment at baseline and follow-up was 2215 of 3076 (71.3%) and 1933 of 2852 (64.8%) for female participants and 2753 of 3196 (85.3%) and 2079 of 2557 (77.3%) for male participants, respectively, with declining standardized prevalence ratios over time (female participant baseline, 1.01; 95% CI, 0.98-1.03; follow-up, 0.94; 95% CI, 0.90-0.98; P < .001; male participant baseline, 0.96; 95% CI, 0.94-0.97; follow-up, 0.92; 95% CI, 0.89-0.95; P = .02). While the prevalence of health-related unemployment increased (female participants, 11.6% to 17.2%; male participants, 8.1% to 17.1%), the standardized prevalence ratio remained higher than the general population and declined over time (female participant baseline, 3.78; 95% CI, 3.37-4.23; follow-up, 2.23; 95% CI, 1.97-2.51; P < .001; male participant baseline, 3.12; 95% CI, 2.71-3.60; follow-up, 2.61; 95% CI, 2.24-3.03; P = .002). Among survivors employed full-time at baseline (1488 female participants; 1933 male participants), 285 female participants (19.2%) and 248 male participants (12.8%) experienced a negative employment transition (median [range] follow-up, 11.5 [9.4-13.8] years). Higher numbers and grades of chronic health conditions were significantly associated with these transitions. Conclusions and Relevance: In this retrospective analysis of adult survivors of childhood cancer, significant declines in employment and increases in health-related unemployment among cancer survivors compared with the general population were identified. A substantial portion of survivors in the midcareer age range fell out of the workforce. Awareness among clinicians, caregivers, and employers may facilitate clinical counseling and occupational provisions for supportive work accommodations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.377
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2024
Admission routes1
Has abstractyes

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