Improving Family Satisfaction Through Conflict-Management Training for the Immanuel Seventh-day Adventist Church in Toronto, Canada
Bibliographic record
Abstract
Problem The Immanuel Seventh-day Adventist Church is a multiracial congregation with members from over 35 countries, with approximately 200 families in regular attendance. Twenty percent of these families are interracial families, while 80% are a balanced mix of ethnic makeup. Reports from leaders and members, as well as pastoral observation, led us to the conclude that many families were affected by family squabbles and separation due to unresolved conflict. Consequently, there was a high level of stress and family dissatisfaction. This resulted in declining participation in ministry and mission. Methods A 10-hour conflict-management education-training program was developed and implemented. The Thomas-Kilmann Conflict Mode Instrument and the Family Satisfaction Scale survey were used to determine family members’ conflict styles and levels of satisfaction within their families, respectively; the Family Satisfaction Scale survey was used for pre- and post-evaluation. A series of four conflict-management seminars were conducted, using the Conflict Workshop Facilitator’s Guide. The participants’ responses to a set of study questions and the survey instruments were used in order to evaluate the impact of the education program on the participants’ experiences. Results Twenty-six participants enrolled in the 10-hour conflict-management education program. Participants reported that their most frequently used conflict mode when handling conflict in the family were avoiding (38%) and accommodating (38%). In addition, family satisfaction levels experienced an overall growth of 41%. Moreover, responses to the research questions show that the conflict-management seminars had a positive impact overall. Participants indicated that an improvement to the program would be to have more time and role playing. Conclusion This study revealed that family members who are intentional and equipped to manage conflict using the skills and modes of the Thomas-Kilmann Conflict Mode Instrument can positively affect conflict management, which leads to improved family satisfaction. Therefore, implementing this educational seminar in other settings has merit and benefits, and I would recommend it.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".