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Record W4391221182 · doi:10.1037/rep0000542

Creative nonfiction approach to explore peer mentorship for individuals with spinal cord injury.

2024· article· en· W4391221182 on OpenAlexafffund
Zhiyang Shi, Jeffrey G. Caron, Jacques Comeau, Pierre Lepage, Shane N. Sweet

Bibliographic record

VenueRehabilitation Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsHEC MontréalCentre for Interdisciplinary Research in RehabilitationMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMentorshipSpinal cord injuryPsychologyPhysical medicine and rehabilitationMedicineSpinal cordPhysical therapyMedical educationNeuroscience

Abstract

fetched live from OpenAlex

PURPOSE: Research has examined peer mentorship to understand how it may help people with spinal cord injury (SCI) adapt and thrive. We still lack an in-depth understanding of the perspectives of SCI peer mentors and mentees on their dyadic relationship. This study was to explore the dyadic interactions and relationships between SCI peer mentors and mentees in a peer mentorship program delivered at a rehabilitation center. RESEARCH METHOD: = 12). Data were analyzed using a creative nonfiction approach. RESULTS: Three unique dialogical stories were developed. Story 1 (A slow and steady start) described how mentors took a mentee-centered approach in building the relationship. Story 2 (Mentorship and friendship: negotiating the "grey zone") highlighted how mentees and mentors experienced challenges in navigating the boundaries between mentorship and friendship. Story 3 (The "endless" job for mentor) showcased how the relationship could enter a phase in which it could affect mentors' well-being. CONCLUSIONS: The stories highlighted important attributes to the relationships between SCI mentors and mentees. Considerations were suggested for community-based SCI organizations to integrate peer mentorship into rehabilitation settings, including optimizing mentorship introductions and matching, defining mentors' role explicitly, and building support systems for mentors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.432
Teacher spread0.342 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2024
Admission routes2
Has abstractyes

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