Effect of an innovative model of complexity care on family caregiver experience: Qualitative study in family practice.
Bibliographic record
Abstract
OBJECTIVE: To investigate the experiences of family caregivers who participated in an innovative model of interprofessional team-based care specifically designed for elderly patients with complex care needs. DESIGN: Qualitative study. SETTING: Large academic family practice in Toronto, Ont. PARTICIPANTS: Family caregivers of elderly patients who had attended the IMPACT (Interprofessional Model of Practice for Aging and Complex Treatments) clinic (N = 13). METHODS: Individual semistructured interviews, which were conducted face-to-face, audiorecorded, transcribed verbatim, and analyzed using the constant comparative method. MAIN FINDINGS: Family caregivers who attended the IMPACT clinic believed it enhanced caregiver experience and capacity. Caregivers experienced increased validation and engagement with the treatment team. Feelings of isolation were reduced, resulting in increased confidence and greater feelings of empowerment in their caregiver role. CONCLUSION: While the needs and value of caregivers are increasingly acknowledged, health care teams continue to struggle with how to relate to and engage with family caregivers-how best to support them and work with them in the context of their family members' care. Interprofessional teams who adopt the IMPACT model-providing synchronous, real-time interventions that include the caregiver-can facilitate increased caregiver capacity, confidence, and empowerment.
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 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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".