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Record W7160645130 · doi:10.1080/13674676.2025.2505583

Enhancing health outcomes in cancer patients: exploring self-forgiveness and the mediating role of spiritual growth through the transcendental self-healing model – a longitudinal study

2025· article· en· W7160645130 on OpenAlexaboutno aff
Sebastian Binyamin Skalski-Bednarz

Bibliographic record

VenueMental Health Religion & Culture · 2025
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsnot available
FundersFundacja na rzecz Nauki PolskiejInternational Visegrad Fund
KeywordsLongitudinal studyCancerTranscendental numberSpiritualityLongitudinal dataCancer survivorship

Abstract

fetched live from OpenAlex

This study explores the role of self-forgiveness and spiritual growth in enhancing health outcomes among cancer patients through a three-wave longitudinal mediation model. Conducted within a Christian demographic in Western Canada, the research involved 212 participants undergoing treatment at oncological rehabilitation centres. Utilising validated scales, the study measured self-forgiveness, spiritual growth, and health outcomes, with data collected at three-month intervals. The findings reveal that spiritual growth significantly mediates the relationship between self-forgiveness and improved mental and physical health. The study highlights the therapeutic potential of integrating spiritual and emotional dimensions into healthcare practices, suggesting that self-forgiveness and spiritual development can play critical roles in patient care and well-being. This research contributes to the literature on psycho-oncology and offers insights into the mechanisms through which spirituality and self-forgiveness impact health outcomes in chronic illness contexts.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.362
Teacher spread0.332 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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