Typology of organizational innovation components: building blocks to improve access to primary healthcare for vulnerable populations
Bibliographic record
Abstract
BACKGROUND: Achieving equity of access to primary healthcare requires organizations to implement innovations tailored to the specific needs and abilities of vulnerable populations. However, designing pro-vulnerable innovations is challenging without knowledge of the range of possible innovations tailored to vulnerable populations' needs. To better support decision-makers, we aimed to develop a typology of pro-vulnerable organizational innovation components - akin to "building blocks" that could be combined in different ways into new complex innovations or added to existing organizational processes to improve access to primary healthcare. METHODS: To develop the typology, we used data from a previously conducted a) scoping review (2000-2014, searched Medline, Embase, CINAHL, citation tracking, n = 90 articles selected), and b) environmental scan (2014, online survey via social networks, n = 240 innovations). We conducted a typological analysis of the data. Our initial typology yielded 48 components, classified according to accessibility dimensions from the Patient-Centred Accessibility Framework. The initial typology was then field-tested for relevance and usability by health system stakeholders and refined from 2014 to 2018 (e.g., combined similar components, excluded non-organizational components). RESULTS: The selected articles (n = 90 studies) and survey responses (n = 240 innovations) were mostly from the USA, Canada, Australia and the UK. Innovations targeted populations with various vulnerabilities (e.g., low income, chronic illness, Indigenous, homeless, migrants, refugees, ethnic minorities, uninsured, marginalized groups, mental illness, etc.). Our final typology had 18 components of organizational innovations, which principally addressed Availability & Accommodation (7/18), Approachability (6/18), and Acceptability (3/18). Components included navigation & information, community health worker, one-stop-shop, case management, group visits, defraying costs, primary healthcare brokerage, etc. CONCLUSIONS: This typology offers a comprehensive menu of potential components that can help inform the design of pro-vulnerable organizational innovations. Component classification according to the accessibility dimensions of the Patient-Centred Accessibility Framework is useful to help target access needs. Components can be combined into complex innovations or added to existing organizational processes to meet the access needs of vulnerable populations in specific contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".