Properties of a provider questionnaire of access to primary health care by vulnerable groups
Bibliographic record
Abstract
Context Vulnerable groups, such as poor, refugee and indigenous communities, experience barriers to access, unmet needs for care, delayed or inappropriate treatments and avoidable hospital use. Most current tools for measuring access draw on patient perspectives. There is a need for tools to capture provider perspectives on the care of vulnerable patients to inform and evaluate interventions. Objective To assess the psychometric properties of a provider questionnaire on access to primary health care by vulnerable groups in Australia. Methods Setting Australian general practices in New South Wales and Victoria; Residential Aged-Care facilities in South Australia recruited to the Canadian-Australian Innovative Models Promoting Access to Care Transformation (IMPACT) study of models for improving access to care for vulnerable groups. Participants GPs and nurses. (N=79): 52% GPs, 48% nurses; 64% female. Instrument The questionnaire was developed using a conceptual model of access and logic models of interventions to improve access and drew on a range of measures developed in Canada and Australia. Seven items dealt with confidence to treat vulnerable patients (“Confidence”) and ten with referral to other services (“Referral”). Main outcome Factor structure of questionnaire items. Analysis Data were screened for missing data, normality and co-linearity. One item with 27% missing data was excluded. Exploratory factor analyses were conducted separately on Confidence and Referral variables. Results The items showed non-normal distribution but no collinearity. Kaiser-Meyer-Olkin and Bartlett’s Test of Sphericity suggested the data were adequate for factor analysis. There was one factor relating to Confidence (81% of variance) and three factors relating to Referral (69% of variance). These showed meaningful content validity. Conclusion This instrument may be useful in measuring provider confidence in managing vulnerable populations and their referral practices. We are now evaluating its utility in informing and evaluating the IMPACT study interventions.
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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 | 0.028 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".