An Interpretative Phenomenological Analysis of the Experience of Mattering in Individuals after a Motor Vehicle Crash
Bibliographic record
Abstract
AbstractThe concept of mattering, or one’s perceived significance to others, has demonstrated importance in the well-being of individuals. This construct, however, has yet to be adequately explored in motor vehicle crash (MVC) and other trauma survivors who are assumed to be in grave need of positive social interactions and support after experiencing a life-changing event(s). Accordingly, the current study is significant because it addresses a significant gap in the literature on the experiences of mattering for MVC survivors. This interpretive phenomenological study aims to understand the lived experience of mattering (and not mattering) as articulated by MVC survivors. Additionally, the study is interested in understanding how these experiences of mattering impacted an individual’s mental health and well-being. Alternatively, how did the individuals feel when the experience of mattering happened? The study also aimed to establish a greater awareness of how the mattering experience impacts a motor vehicle crash survivor’s recovery. This interpretive phenomenological study included a purposeful sample of nine participants from Canada and the United States who have experienced a MVC. Data was collected through semi-structured interviews. Data analysis included data ciioding to establish themes. Information was reported through methods that included deconstruction, bracketing, and cross-case analysis. The study revealed three major themes for motor vehicle crash survivors: impact on recovery, other's investment in us, and impact on others. This study implies that when individuals after a MVC experience mattering, they are more likely to have enhanced well-being and positive outcomes for recovery. This study is significant because it has the potential to disseminate findings of the study to healthcare professionals working with clients after a MVC for their personal reflection in relation to their current understanding of the phenomenon of mattering and where appropriate, utilize such knowledge to guide their practice. The findings have the potential to inform health care policy and professional health care practice concerning the experience of mattering for individuals after a MVC. Keywords: well-being, motor vehicle crash, recovery, mattering, significance to others, interpretive phenomenological study.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".