doi:10.1155/2010/491368 Research Article Mentoring Experiences of Aging and Disability Rehabilitation
Bibliographic record
Abstract
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objectives. To explore research mentoring experiences and perceived mentoring needs of aging and disability researchers at different career stages. Design. Focus group and individual interviews with rehabilitation researchers at various career stages based in hospitals, universities, and hospital-based research institutes in Ontario, Canada. Results. The overall theme was mentoring for transition. Participants across career stages referred to helpful mentoring experiences as those that assisted them to move from their previous stage into the present stage or from the present stage into their next career progression. Unhelpful mentoring experiences were characterized by mentor actions that were potentially detrimental to transition. Subsumed under this theme were three categories. The first, “hidden information ” referred to practical information that was difficult to access. The second “delicate issues ” referred to helping the participant work through issues related to sensitive matters, the discussion of which could put the participants or their colleagues in a vulnerable position. The third category was “special challenges of clinicianresearchers”. Conclusions. Helpful mentoring for rehabilitation researchers working on concerns related to aging and disability
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.851 | 0.677 |
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".