MétaCan
Menu
Back to cohort
Record W4317600104 · doi:10.53379/cjcd.2023.348

Career Counselling for Cancer Survivors Returning to Work

2023· article· en· W4317600104 on OpenAlex
Charles P. Chen, Deana Slater

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Career Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorkforceCoping (psychology)Career counselingPsychologyWork (physics)Career PathwaysMeaning (existential)Psycho-oncologyMedical educationNursingPsychotherapistMedicineApplied psychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Cancer impacts workability significantly more than other physical and psychiatric disorders. Accommodations are often required upon returning to work after treatment, and cancer survivors may experience discrimination during this process. This article discusses key career challenges cancer survivors face and presents relevant career counselling theories to assist clients in navigating them. Constructivist career counselling models and happenstance theory offer strategies to help survivors make meaning out of unexpected events, explore new possibilities for returning to work, and gain skills for coping with future challenges in the workforce.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.075
GPT teacher head0.297
Teacher spread0.222 · 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