THE UNBEARABLE LIGHTNESS OF BEING RETIRED: A QUALITATIVE STUDY OF RETIRED CHIEF EXECUTIVE OFFICERS
Bibliographic record
Abstract
There is a lightness that can be associated with relinquishing the responsibilities of work in retirement, which for some individuals can be unbearable. This study explored the work and retirement transitions of retired Chief Executive Officers (CEOs) from healthcare organizations to examine their perceptions about being retired. In-depth, in-person interviews were conducted with 26 retired CEOs of healthcare organizations using an interview guide that was informed by the life course theoretical perspective to explore participants’ career trajectories and retirement experiences. Verbatim transcripts, notes, and career trajectory maps were analyzed making use of thematic analysis. In the wake of trailblazing careers, participants associated feelings of unbearable lightness and ennui with being retired. Participants acquiesced to societal pressures to retire, either by choice or force, despite their desires to continue to achieve personal fulfillment through work. These findings diversify knowledge of contemporary retirement by illustrating how anachronistic and ageist notions of retirement as an age-graded exit from employment can threaten an individual’s sense of self and foreshortened workforce contributions. Participants desire to be fulfillment employed instead of retired reinforce the notion that retirement is better understood as a liminal or threshold stage where an individual contemplates his or her next step and even whether the status will hold.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".