To ensure carers of people with dementia receive support, community services increasingly use measures of caregiver (carer) burden to assess for unmet need. This study used Bradshaw's taxonomy of need to explore the link between measures of carer burden (normative need), service use (expressed need), and carer's stated need (felt need). This mixed method exploratory study compared measures of carer burden with community services received and unmet needs, for 20 community-dwelling carer/care-recipient pairs. A simple one-item measure of carers' felt need for more services was significantly related to carer stress as measured on the GHQ-30. Qualitative data showed that there are many potential stressors for carers, other than those related to the care-giving role. We found a statistically significant rank correlation (p = 0.01) between carer's use of in-home respite and the care-recipient's cognitive and functional status which is likely to have been related to increased requirement for carer vigilance, effort and the isolation of spouse carers. Otherwise, there were no statistically significant relationships between carer burden or stress and level of service provision. When carers are stressed or depressed, they can recognise that they would like more help from services, even if measures of carer burden and care recipient status do not clearly indicate unmet service needs. A question designed to elicit carer' felt need may be a better indicator of service need, and a red flag for recognising growing stress in carers of people with dementia. Assessment of service needs should recognise the fallibility of carer burden measures, given that carer stress may not only come from caring for someone with dementia, but can be significantly compounded by other life situations.

JMIR Human Factors - a leading peer-reviewed ehealth journal

Most countries face an ageing population, increasing chronic diseased, and constrictions on budget for providing health services. Involving patients in their own care by allowing them access to their patient data is a trend seen in many places. Data on the type and level of access citizens have to their own health data in three countries was gathered from public sources. Data from each individual country is presented and the experiences of Denmark, Estonia and Australia are examined whilst similarities and differences explored. The discussion adopts a citizen-centred perspective to consider how the different e-portal systems support, protect and structure citizen interactions with their own health data in three key areas: Security, privacy and data protection; User support; and Citizen adoption and use. The paper highlights the impact of opt-in/opt-out approaches on citizen access and the lack of a structured approach to addressing differences in citizen health and e-health literacy. This research also confirms while current data provides detail on the availability and use of personal health data by citizens, questions still remain over the ultimate impact on patient outcomes of these initiatives. It is anticipated the insights generated from the three countries experiences, supporting citizen access to their health data will be useful to improve these initiatives and guide other countries aspiring to support similar initiatives.

Forest inventories play an important role in enabling informed decisions to be made for the management and conservation of forest resources; however, the process of collecting inventory information is laborious. Despite advancements in mapping technologies allowing forests to be digitized in finer granularity than ever before, it is still common for forest measurements to be collected using simple tools such as calipers, measuring tapes, and hypsometers. Dense understory vegetation and complex forest structures can present substantial challenges to point cloud processing tools, often leading to erroneous measurements, and making them of less utility in complex forests. To address this challenge, this research demonstrates an effective deep learning approach for semantically segmenting high-resolution forest point clouds from multiple different sensing systems in diverse forest conditions. Seven diverse point cloud datasets were manually segmented to train and evaluate this model, resulting in per-class segmentation accuracies of Terrain: 95.92%, Vegetation: 96.02%, Coarse Woody Debris: 54.98%, and Stem: 96.09%. By exploiting the segmented point cloud, we also present a method of extracting a Digital Terrain Model (DTM) from such segmented point clouds. This approach was applied to a set of six point clouds that were made publicly available as part of a benchmarking study to evaluate the DTM performance. The mean DTM error was 0.04 m relative to the reference with 99.9% completeness. These approaches serve as useful steps toward a fully automated and reliable measurement extraction tool, agnostic to the sensing technology used or the complexity of the forest, provided that the point cloud has sufficient coverage and accuracy. Ongoing work will see these models incorporated into a fully automated forest measurement tool for the extraction of structural metrics for applications in forestry, conservation, and research.

Objectives To assess benefits of telephone-delivered health mentoring in community-based chronic obstructive pulmonary disease (COPD). Design Cluster randomised controlled trial. Setting Tasmanian general practices: capital city (11), large rural (3), medium rural (1) and small rural (16). Participants Patients were invited (1207) from general practitioner (GP) databases with COPD diagnosis and/or tiotropium prescription, response rate 49% (586), refused (176) and excluded (criteria: smoking history or previous study, 68). Spirometry testing (342) confirmed moderate or severe COPD in 182 (53%) patients. Randomisation By random numbers code, block stratified on location, allocation by sequentially numbered, opaque and sealed envelopes. Intervention Health mentor (HM) group received regular calls to manage illness issues and health behaviours from trained community health nurses using negotiated goal setting: problem solving, decision-making and action planning. Control: usual care (UC) group received GP care plus non-interventional brief phone calls. Outcomes Measured at 0, 6 and 12 months, the Short Form 36 (SF-36) and St George’s Respiratory Questionnaire (SGRQ, primary); Partners In Health (PIH) Scale for self-management capacity, Hospital Anxiety and Depression Scale (HADS), Center for Epidemiologic Studies-Depression (CES-D) questionnaire, Post-Traumatic Stress Disorder Checklist, Satisfaction with life and hospital admissions (secondary). Results 182 participants with COPD (age 68±8 years, 62% moderate COPD and 53% men) were randomised (HM=90 and UC=92). Mixed model regression analysis accounting for clustering, adjusting for age, gender, smoking status and airflow limitation assessed efficacy (regression coefficient, β, reported per 6-month visit). There was no difference in quality of life between groups, but self-management capacity increased in the HM group (PIH overall 0.15, 95% CI 0.03 to 0.29; knowledge domain 0.25, 95% CI 0.00 to 0.50). Anxiety decreased in both groups (HADS A 0.35; 95% CI −0.65 to −0.04) and coping capacity improved (PIH coping 0.15; 95% CI 0.04 to 0.26). Conclusions Health mentoring improved self-management capacity but not quality of life compared to regular phone contact, which itself had positive effects where decline is generally expected.