Healthcare perceptions of persons with traumatic brain injuries across providers: shortcomings in the chronic phase of care
Bibliographic record
Abstract
Objective This investigation sought to examine the perceptions of individuals with mild, moderate, and severe traumatic brain injury (TBIs) toward healthcare providers across rehabilitation contexts (acute, rehabilitation, and chronic).Methods The mCARE-TBI survey was distributed via Qualtrics throughout the US and Canada. Sixty-four survey responses met criteria for analysis. Participants were ≥18 years old, one-year post onset, reported no unrelated neurological deficits prior to the single TBI, and reported no prior diagnosed psychiatric disorders. The mCARE was the primary outcome measure. Comparisons were made between provider ratings on each question.Results Significant differences were found between chronic-phase care, compared to acute care and rehabilitation. Group differences were found in transitioning home after discharge and in between each therapy discipline and both nurses and doctors, as well as for really listening and pairwise differences between therapy disciplines and both nurses and doctors. Group effects were found for showing compassion and care and being positive.Conclusions All disciplines need to improve communication, and transition care/discharge planning. Dismissal of ongoing impairments continues to be an area of perceived concern with doctors in particular. Communication training is needed, particularly for nurses and doctors.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".