Assessing care-giving demands, resources and costs of family/friend caregivers for persons with mental health disorders: A scoping review
Bibliographic record
Abstract
As mental health (MH) care has shifted from institutional settings to the community, families and friends are responsible for providing the majority of the care at home. The substantial literature on the adverse effects experienced by caregivers has focused mainly on psychological morbidity. Less attention has been paid to how caregivers for persons with MH disorders interact with larger social systems and the impacts of factors such as financial strain, lost time from leisure activities, and the availability of health and social services. We conducted a scoping review of MH and other caregiver questionnaires published between 1990 and 2016 to determine whether they addressed four key domains: caregiver work demands, resource needs, resource utilisation and costs. A range of health and social care databases were searched, including MEDLINE and Health and Psychosocial Instruments. After screening for relevance and quality, our search identified 14 instruments addressing elements related to one or more of our domains. Because these instruments covered only a small portion of our domains, we conducted a second targeted search of the general care-giving literature and consulted with experts, identifying an additional 18 instruments. A total of 32 questionnaires were reviewed, 14 specific to care-giving for mental health problems and 18 for other health conditions. Our search identified instruments or items within instruments that assess constructs in each of our domains, but no one instrument covered them completely. Additionally, some constructs were evaluated in detail and others only addressed by single items. While these instruments are helpful for moving measurement beyond the psychological impacts of care-giving, our results serve only as an initial guide. Additional methodological work is needed to more comprehensively measure the impact of care-giving for individuals with MH disorders and to contribute to the development of more meaningful and effective policies and programmes.
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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".