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 <i>personal information</i>, <i>recreational programs</i>, and <i>chronic health services</i> for mentors and mentees. The most frequently coded techniques were <i>giving personal information, social smoothers</i>, and <i>closed question</i> for mentors; and <i>giving personal information</i>, <i>social smoothers</i>, and <i>sharing perspective</i> 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,563 | 0,003 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».