Retrospective review of work transition narratives: Advancing occupational perspectives and strategies
Bibliographic record
Abstract
BACKGROUND: In 2009 the journal WORK commenced a new column for the publication of Work Transition Narratives. Fourteen persons with lived experience published their narratives on approaches that helped them through work disruptions and change. OBJECTIVE: A review of the articles was conducted to understand how people navigated challenges and obstacles and made sense of their in-transition experiences to return to work or to find new employment. METHODS: A retrospective review was conducted using a convenience sample of N = 14 published narratives. A template approach was developed using micro (individual) and macro (social, cultural, political, structural) level issues to extract and analyze descriptive content. A senior researcher and two Masters of Science students independently reviewed the narratives and extracted data. A dialogic and inductive approach was used to achieve consensus on the description of the types of mechanisms used to move forward. RESULTS: The mechanisms evident in the narratives used by people to navigate work disruptions included drawing on anchors, catalysts, champions, opportunities, learning, coming to terms, critical conversations, and critical reflections. CONCLUSION: Mechanisms used to navigate in-transition experiences add to the knowledge on negotiating the dialectical relationship of micro and macro level challenges in occupational transitions of work. This review and analysis revealed commonly used strategies that may assist others in addressing in-transition work challenges. In addition, the findings have implications for ongoing research and the development of occupational mindfulness approaches that may help people through the overwhelming and often daunting experience of work transitions.
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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 | 0.069 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".