Understanding and reducing compassion fatigue using the compassion fatigue resilience model and self-compassion in peer mentors of Canadian spinal cord injury community service organizations
Notice bibliographique
Résumé
To foster adaptation and thriving among individuals with a spinal cord injury (SCI), peer support programs were developed by Canadian SCI community service organizations. Individuals who serve as peer mentors in these programs have reported experiencing compassion fatigue, a state of exhaustion from prolonged exposure to suffering/stress and diminished mental health (i.e., a state of psychological, emotional, and social well-being). Consequently, these effects are leading peer mentors to resign from their roles, presenting a significant challenge for SCI organizations. According to the compassion fatigue resilience model (CFRM), there are factors that can make individuals more susceptible or resilient to compassion fatigue. Self-compassion, a healthy way of relating to the self, is an important factor for reducing compassion fatigue and improving mental health among individuals in caring roles and could be important to consider within the CFRM and for peer mentors. The overall purpose of this doctoral thesis was to understand and reduce compassion fatigue among peer mentors within Canadian SCI community service organizations via a self-compassion program. We collaborated with SCI British Columbia and SCI Ontario while adhering to the integrated knowledge translation guiding principles for SCI research. Chapter 3 (Article 1) adopted a generic qualitative design and used the CFRM and self-compassion theory to understand the experiences of compassion fatigue (resilience) among eight experienced peer mentors within SCI organizations. Based on personal diary and interview data, peer mentors whose experiences aligned with compassion fatigue reported feeling physically, psychologically, and emotionally exhausted, impacting their perceived effectiveness as a peer mentor. Having traumatic memories and lack of self-compassion contributed to compassion fatigue. Conversely, self-compassion promoted resilience. Chapter 4 (Article 2) discusses the protocol used to develop and examine the feasibility, acceptability, and effectiveness of a tailored self-compassion program in improving compassion fatigue, compassion satisfaction, self-compassion, and mental health among peer mentors. With the two SCI organizations, we used an iterative approach to codeveloping the program using results from Chapter 3. Prior to conducting a full evaluation, we implemented the program with two organizational staff and two peer mentors from SCI organizations with knowledge on self-compassion. This feedback helped to further tailor the program and make it more relevant for peer mentors. Chapter 5 (Article 3) used a mixed method approach (i.e., surveys and interviews) to empirically explore the feasibility, acceptability, and effectiveness of the self-compassion program in improving compassion fatigue, compassion satisfaction, self-compassion, and mental health among fifteen peer mentors. The program proved to be feasible and acceptable, providing opportunities for peer mentors to connect with others who have shared lived experience. Compassion fatigue, self-compassion, and mental health improved from pre to post with small-to-medium effects (|r|=.12-.47) and pre to 6-week follow-up with small-to-large effects (|r|=.13-.56). Four themes were identified at post including: from a self-critic to a self-ally, being a better peer mentor, building resilience, and benefits to the organization. Taken together, this thesis provides insight that (1) compassion fatigue is a complex and multifaceted process, with self-compassion being critical to building resilience and (2) interventions to reduce compassion fatigue should be tailored to the needs of the population and implement strategies that promote resilience, such as self-compassion. This thesis contributed to the literature by exploring self-compassion within the CFRM, informing the development of a tailored intervention that can promote resilience towards compassion fatigue and improve mental health among peer mentors within SCI peer support programs
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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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».