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Record W2037563747 · doi:10.1155/2010/491368

Mentoring Experiences of Aging and Disability Rehabilitation Researchers

2010· article· en· W2037563747 on OpenAlexaffabout
Mary C. Egan, Kerry Byrne, Paul Stolee, Judy King

Bibliographic record

VenueRehabilitation Research and Practice · 2010
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversity of WaterlooUniversity of British ColumbiaÉlisabeth Bruyère HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineRehabilitationGerontologyPhysical medicine and rehabilitationNursingPhysical therapy

Abstract

fetched live from OpenAlex

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 clinician-researchers". Conclusions. Helpful mentoring for rehabilitation researchers working on concerns related to aging and disability appears to be characterized by interaction with more experienced individuals who aid the researcher work through issues related to career transition.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.119
GPT teacher head0.502
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes2
Has abstractyes

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