The Impact of Spirituality on Emotional Healing: A Qualitative Perspective
Bibliographic record
Abstract
Objective: This study aimed to explore the impact of spirituality on emotional healing, examining how spiritual practices, beliefs, and social contexts contribute to individuals' emotional recovery processes. Methods and Materials: A qualitative research design was employed, utilizing semi-structured interviews with 33 participants from Canada. Participants were selected based on their self-identified spiritual practices and experiences of emotional healing. Data were analyzed using NVivo software, and theoretical saturation was achieved through in-depth exploration of participants' narratives. The study focused on identifying key themes related to the role of spirituality in emotional well-being. Findings: The study revealed five main themes: 1) Spiritual practices as coping mechanisms, including prayer, meditation, and scripture reading; 2) Transformation through spiritual insight, such as increased self-awareness and forgiveness; 3) Emotional healing in social-spiritual contexts, involving community support and shared testimonies; 4) Spiritual identity and emotional resilience, highlighting the role of a stable spiritual self in navigating emotional challenges; and 5) Challenges in spiritual healing, including spiritual doubt and conflicts with secular norms. Participants consistently reported that spiritual practices facilitated emotional regulation, provided meaning, and contributed to post-traumatic growth, while some also expressed difficulties with negative religious experiences. Conclusion: Spirituality plays a significant role in emotional healing, offering coping mechanisms, emotional resilience, and a transformative sense of purpose. However, its impact is complex, as negative spiritual experiences may also hinder emotional recovery. Practitioners should adopt a flexible, person-centered approach to integrating spirituality into emotional healing processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".