Investigating the adaptation of caregivers of people with traumatic brain injury: a journey told in evolving research traditions
Bibliographic record
Abstract
PURPOSE: To examine how conceptualizations of caregiver adaptation to traumatic brain injury have changed over time. The objectives were to identify research traditions, adaptive outcomes assessed in these traditions, and psychosocial variables associated with adaptive outcomes. METHODS: A meta-narrative review was conducted on 29 identified articles published over a 25-year period (1990-2015). RESULTS: Four traditions were identified with varying storylines. Burden/Strain (1990-1999) focused on adjustment as the absence of a negative state. Appraisal/Coping (2000-2005) recognized that caregiving experiences could be both positive and negative. In Quality of Life (2006-2011), there was increasing recognition that both personal and contextual factors influence adaptation. Resiliency (2012-2015) used the term "resiliency" as an organizing framework for a broad group of variables and assessed resilience, quality of life, community re-integration, and life/marital satisfaction. CONCLUSIONS: These storylines reflect an evolution from problem-based to strengths-based conceptualizations, from interest in crisis to considering adaptation as a process unfolding over time, from quantitative to qualitative methods, and towards more holistic views of adaptive outcomes. Variables significantly associated with outcomes across the traditions included social support, reframing and positive appraisal, and behavior strategies. Implications concern the need for longitudinal studies, measurement of environmental factors, and the development of best practices.IMPLICATIONS FOR REHABILITATIONResearch studies on the adaptation of caregivers for people with TBI have evolved from a focus on burden, to coping and quality of life, and most recently to resiliency.It is important to assist caregivers of people with TBI to obtain social support, find positive ways of viewing their experiences, and take part in respite and enjoyed activities.Service providers can help caregivers by adopting a strengths-based perspective to help them recognize available resources, supports, and opportunities.Since caregiver adaptation changes over time, service providers should pay attention to changes in family circumstances and the mental health of caregivers.
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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 | 0.053 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".