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Cancer, Work, and the Quality of Working Life

2014· other· en· W1589314499 on OpenAlexaboutno aff
Tom Cox, Sara MacLennan, James N’Dow

Bibliographic record

VenueWell Being · 2014
Typeother
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)NarrativeQuality of life (healthcare)Relation (database)Work (physics)PsychologySubject (documents)Public relationsPolitical sciencePsychotherapistComputer scienceEngineering

Abstract

fetched live from OpenAlex

This chapter is concerned with the relationships among cancer survivorship, working life, and wellbeing. It presents a narrative review of the evidence published on this important subject within the framework of the person × environment model, exploring the Accommodation Adaptation Intervention Paradigm being developed by the authors through theMETISCollaboration in the United Kingdom. The focus is on the psychological, social, and organizational issues involved across the patient journey. The review suggests that there is still much to be understood about the successful maintenance of working life in those with cancer and about the respective roles of the key stakeholders, including employing organizations. However, there is much that can be achieved, based on the existing evidence, particularly in relation to the interactions among the stakeholders. Furthermore, what is being learnt here may logically be applied to managing other chronic conditions in relation to working life and wellbeing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.048
GPT teacher head0.323
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2014
Admission routes1
Has abstractyes

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