Investigating the peer Mentor-Mentee relationship: characterizing peer mentorship conversations between people with spinal cord injury
Notice bibliographique
Résumé
This study aimed to: (1) develop a coding manual to characterize topics discussed and conversation techniques used during peer mentorship conversations between people with spinal cord injury (SCI); (2) assess the reliability of the manual; and (3) apply the manual to characterize conversations. The study was conducted in partnership with three Canadian provincial SCI organizations. Twenty-five phone conversations between SCI peer mentors and mentees were audio-recorded and transcribed verbatim. Ten transcripts were inductively analyzed to develop a coding manual identifying topics and techniques used during the conversations. Inductive technique codes were combined and deductively linked to motivational interviewing and behaviour change techniques. Two coders independently applied the coding manual to all transcripts. Code frequencies were calculated. The coding manual included 14 topics and 31 techniques. The most frequently coded topics were personal information, recreational programs, and chronic health services for mentors and mentees. The most frequently coded techniques were giving personal information, social smoothers, and closed question for mentors; and giving personal information, social smoothers, and sharing perspective for mentees. This research provides insights into topics and techniques used during real-world peer mentorship conversations. Findings may be valuable for understanding and improving SCI peer mentorship programs.Implications for RehabilitationSCI peer mentorship conversations address a wide range of rehabilitation topics ranging from acute care to living in the community.Identification of the topics discussed, and techniques used in SCI peer mentorship conversations can help to inform formalized efforts to train and educate acute and community-based rehabilitation professionals.Identifying commonly discussed topics in SCI peer mentorship conversation may help to ensure that peer mentors are equipped with the necessary knowledge and resources, or the development of those resources be prioritized.Developing a method to characterize the topics discussed and techniques used during SCI peer mentorship conversations may aid in designing methods to evaluate how rehabilitation professionals provide support to people with SCI. SCI peer mentorship conversations address a wide range of rehabilitation topics ranging from acute care to living in the community. Identification of the topics discussed, and techniques used in SCI peer mentorship conversations can help to inform formalized efforts to train and educate acute and community-based rehabilitation professionals. Identifying commonly discussed topics in SCI peer mentorship conversation may help to ensure that peer mentors are equipped with the necessary knowledge and resources, or the development of those resources be prioritized. Developing a method to characterize the topics discussed and techniques used during SCI peer mentorship conversations may aid in designing methods to evaluate how rehabilitation professionals provide support to people with SCI.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».