(Re)constructing the meaning of work: experiences of internationally trained female physicians who immigrate to Canada
Bibliographic record
Abstract
People often derive a great deal of meaning from their work (Brief & Nord, 1990; Wrzesniewski, 2003) and come to define themselves by what they do for a living (Becker, 1970). Consequently, when people immigrate and are unable to resume their occupation of choice or profession by training, they are forced, to some extent, to redefine the meaning of work in their lives and who they are as a productive member of society. Aycan and Berry (1996) found that employment difficulties negatively impact the physical and psychological well being of immigrants. However, the complex process of meaning reconstruction that immigrants go through following loss of profession and the implications this has on immigrants' professional identities is less well understood. My dissertation examines how internationally trained female physicians reconstruct the meaning of work and their professional identity in response to loss of profession following immigration to Canada. Comparative narrative analyses were conducted on interviews with two samples of internationally trained female physicians who had been in Canada for more than two years; eight women who were pursuing medical licensure and eight women who were not pursuing licensure.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".