Bridge employment of Retired Teachers: Fancy or Necessity
Bibliographic record
Abstract
The purpose of this phenomenological study was to explore the lived experiences of 16 retired teachers, 60 years and older, with pension benefits and had an involvement in a bridge employment. Through in-depth interviews of 10 informants and focused group discussion of six participants, the data were gathered and subjected to thematic analysis. The results revealed that the retired teachers decided to take bridge employment for reasons of financial security, passion for the teaching profession, pleasure and satisfaction, ease and simplicity, and need to be relevant and of service. Their coping strategies included making their bridge job simple and uncomplicated, planning and preparation, updating and relearning, being pliant and flexible, resourceful and creative, and securing family assistance and support. The insights they shared conveyed their self-realizations to be financially astute and prudent, internalize that retirement is just a phase in life, and continue to long for life’s meaning and significance, and celebrate life and move on. What is notable in this study is the participants’ being still active and generative, their resiliency to face the challenges, and their insights of wisdom, hope, and faith.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".