Return to work in head and neck cancer survivors: an exploratory multimethod study at a cancer centre in Santiago, Chile
Bibliographic record
Abstract
Objective: Head and neck cancer (HNC) survivors may suffer from functional and psychosocial impairment, and thus, return to work (RTW) often poses challenges. A paucity of evidence on this subject exists in Chile and the region. The aim of this paper is to describe and characterize the RTW of HNC survivors treated at a cancer centre in Santiago. Methods: This study employed an exploratory, cross-sectional design, with a multimethod, quantitative approach. Surgically treated patients with HNC between 2016 and 2022 were invited to participate. Clinical and sociodemographic data were statistically analysed to establish associations with RTW. Participants were surveyed about their process of RTW and income variation. Results: = 0.046). No association was found between disease status, tumour location or treatment received and RTW. Of those who resumed working, a third had less income. Job accommodations were made on a case-by-case basis. A third of the survivors decreased their workload. Conclusion: Being a woman was associated with less RTW. Future interventions should provide support in reintegration into the workplace. This study constitutes the first published data on RTW in Chilean patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".