Bibliographic record
Abstract
This qualitative study explored the career experiences of breast cancer survivors after their diagnosis. Previous literature addressed the post-cancer return to work rates and specific work accommodations, yet little was known about how breast cancer affects women’s meaning of work and how they dynamically utilise and adjust their personal and contextual resources to overcome this obstacle. This study aimed to understand the career experiences of breast cancer survivors, including; the role of personal agency, the shifting career meaning and the contextual influences that impact their return to work. This is also the first study to utilise the Career Human Agency Theory (CHAT) as a framework for the work experiences of cancer survivors and to gather empirical evidence for this emerging meta-theory. Twelve women, ranging in age from 34 to 61, shared their career stories since being diagnosed with breast cancer within the past five years. Open-ended, semi-structured interviews were completed with each participant and then analysed using interpretive phenomenological analysis. Shared themes include changes in their sense of self-efficacy, shifted career priorities and goals, and common factors that supported or hindered their process of returning to work. A unique theme that emerged was the importance of having a sense of belonging at work; as an indicator of meaningful work, a motivator to return, and an integral contextual factor. The theoretical implications include a proposed extension of the CHAT model specific to a population of cancer survivors (CHAT-CS). This model details the relevant contextual barriers and supports which must be considered in the working-lives of cancer survivors. The model provides useful considerations for clinicians and practitioners working with survivors on their career planning and goals. Lastly, the limitations and directions for future research are discussed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".