Caregiver identity in care partners of persons living with mild cognitive impairment
Bibliographic record
Abstract
Research on caregiver identity in the context of memory impairment has focused primarily on more advanced stages of the cognitive impairment trajectory (e.g., dementia caregivers), failing to capture the complex dynamics of early caregiver identity development (e.g., MCI; mild cognitive impairment caregivers). The aim of this study was to develop a nuanced understanding of how caregiver identity develops in family and friends of persons living with MCI. Using constructivist grounded theory (ConGT), this study explored caregiver identity development from 18 in-depth interviews with spouses ( n = 13), children ( n = 3), and friends ( n = 2) of persons recently diagnosed with MCI. The overarching themes influencing MCI caregiver identity development included MCI changes, care-related experiences, “caregiver” interpretation, and approach/avoidance coping. These themes influenced how participants primarily identified, represented as I am a caregiver, I am not a caregiver, or liminality (i.e., between their previous identity and a caregiver identity). Irrespective of their current self-identification, all conveyed thinking about their “future self,” as providing more intensive care. MCI caregiver identity development in family and friends is a fluid and evolving process. Nearly all participants had taken on care tasks, yet the majority of these individuals did not clearly identify as caregivers. Irrespective of how participants identified, they were engaging in care, and would likely benefit from support with navigating these changes and their new, ambiguous, and evolving roles.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".