Taking the pulse of the health services research community: a cross-sectional survey of research impact, barriers and support
Bibliographic record
Abstract
Objective This study reports on the characteristics of individuals conducting health service research (HSR) in Australia and New Zealand, the perceived accessibility of resources for HSR, the self-reported impact of HSR projects and perceived barriers to conducting HSR. Methods A sampling frame was compiled from funding announcements, trial registers and HSR organisation membership. Listed researchers were invited to complete online surveys. Close-ended survey items were analysed using basic descriptive statistics. Goodness of fit tests determined potential associations between researcher affiliation and access to resources for HSR. Open-ended survey items were analysed using thematic analysis. Results In all, 424 researchers participated in the study (22% response rate). Respondents held roles as health service researchers (76%), educators (34%) and health professionals (19%). Most were employed by a university (64%), and 57% held a permanent contract. Although 63% reported network support for HSR, smaller proportions reported executive (48%) or financial (26%) support. The least accessible resources were economists (52%), consumers (49%) and practice change experts (34%); researchers affiliated with health services were less likely to report access to statisticians (P<0.001), economists (P<0.001), librarians (P=0.02) and practice change experts (P=0.02) than university-affiliated researchers. Common impacts included conference presentations (94%), publication of peer-reviewed articles (87%) and health professional benefits (77%). Qualitative data emphasised barriers such as embedding research culture within services and engaging with policy makers. Conclusions The data highlight opportunities to sustain the HSR community through dedicated funding, improved access to methodological expertise and greater engagement with end-users. What is known about the topic? HSR faces several challenges, such as inequitable funding allocation and difficulties in quantifying the effects of HSR on changing health policy or practice. What does this paper add? Despite a vibrant and experienced HSR community, this study highlights some key barriers to realising a greater effect on the health and well-being of Australian and New Zealand communities through HSR. These barriers include limited financial resources, methodological expertise, organisational support and opportunities to engage with potential collaborators. What are the implications for practitioners? Funding is required to develop HSR infrastructure, support collaboration between health services and universities and combine knowledge of the system with research experience and expertise. Formal training programs for health service staff and researchers, from short courses to PhD programs, will support broader interest and involvement in HSR.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".