Telephone triage of suspected head and neck cancer patients during the coronavirus disease 2019 pandemic using the Head and Neck Cancer Risk Calculator version 2.

OBJECTIVE: Utilisation of the Head and Neck Cancer Risk Calculator version 2 has been recommended during the coronavirus disease 2019 pandemic for the assessment of head and neck cancer referrals. As limited data were available, this study was conducted to analyse the use of the Head and Neck Cancer Risk Calculator version 2 in clinical practice. METHOD: Patients undergoing telephone triage in a two-week wait referral clinic were included. Data were collected and analysed using appropriate methods. RESULTS: Sixty-four patients in the study were risk-stratified into low-risk (51.6 per cent, 33 of 64), moderate-risk (14.1 per cent, 9 of 64) and high-risk (34.4 per cent, 22 of 64) groups. Of the patients, 53.1 per cent (34 of 64) avoided an urgent hospital visit, and 96.9 per cent (62 of 64) were cancer free, while 3.1 per cent (2 of 64) were found to have a head and neck malignancy. The sensitivity, specificity, negative predictive value and accuracy were 50.00 per cent, 66.13 per cent, 99.92 per cent and 66.11 per cent, respectively. CONCLUSION: It is reasonable to use the calculator for triaging purposes, but it must always be accompanied by a meticulous clinical thought process.

Training prostate cancer survivors and caregivers to be peer navigators: a blended online/in-person competency-based training program.

PURPOSE: Cancer navigation improves access to support and reduces barriers to care; however, appropriate training of navigators is essential. We developed the TrueNTH Peer Navigation Training Program (PNTP), a competency-based, blended online/in-person course. In this study, we evaluate the feasibility, acceptability, and effectiveness of the PNTP among prostate cancer (PC) survivors (patients, caregivers). METHODS: We employed an explanatory mixed method study design consisting of course usage data, pre-/post-questionnaires, and focus groups informed by the Kirkpatrick framework and self-efficacy theory. RESULTS: Three cohorts in two Canadian cities (n = 26) received the PNTP. Participants were motivated to support others like themselves (n = 20), fill a gap (n = 7), pay it forward (n = 6), and offer expertise (n = 4). Recruitment, retention, and questionnaire completion were 96.7%, 89.6%, and 92%. Participants contributed a total of 426 posts to the online forums (2 to 3 posts per participant/module). Satisfaction was 9.4/10 (SD = 0.7) and usability was 84.5/100 (SD = 10.1). All learning outcomes increased: understanding of learning objectives t(23) = - 6.12, p < 0.0001; self-efficacy to perform competencies t(23) = - 4.8, p < 0.0001; and eHealth literacy t(23) = - 4.4, p < 0.0001. Participants viewed the PTNP as intensive but manageable, improving knowledge and confidence and enhancing listening skills. Participants valued the flexibility of online learning, interactive online learning, in-person interactions for relationship building, and authentic role-playing for skill development. CONCLUSIONS: A facilitated online training program with in-person components is a highly acceptable and effective format to train PC survivors to become peer navigators. This competency-based peer navigator training program and delivery format may serve as a useful model for other cancer volunteer programs.

A core competency framework for prostate cancer peer navigation.

PURPOSE: Trained peer navigators can offer valuable peer support and mentorship to cancer patients and caregivers due to their highly relevant and unique perspective about the disease experience. In order to define the role of prostate cancer (PC) peer navigators within the cancer care system, it is important to establish the essential competencies of a PC peer navigator. We systematically identified and verified a set of core competencies for PC peer navigators and present a competency framework for PC peer navigators. METHODS: In phase 1, we conducted formative research consisting of a literature review and environmental scan as well as a secondary analysis of qualitative interviews. In phase 2, we drafted and mapped competencies. Finally in phase 3, expert stakeholders completed an anonymous survey to indicate whether they endorsed the competencies and to rank the importance of each competency to the peer navigator role. Open-ended feedback was also provided for each competency. RESULTS: Six core competency domains emerged: (1) self as navigator, (2) communication, (3) knowledge/information, (4) facilitate patient-centred care, (5) eHealth/technology, and (6) caregiver needs. Forty-seven core competency statements were mapped to these domains. Expert stakeholders (n = 27) included cancer survivors, caregivers, and healthcare providers. Most (89%) of core competency statements were endorsed by stakeholders and received high priority ratings, whereas only five of the competencies were less uniformly endorsed. CONCLUSIONS: This is the first attempt to list core competencies for PC peer navigators and may offer guidance for standardizing the PC peer navigator role and training.

Valuation of milk composition and genotype in cheddar cheese production using an optimization model of cheese and whey production.

A mass balance optimization model was developed to determine the value of the kappa-casein genotype and milk composition in Cheddar cheese and whey production. Inputs were milk, nonfat dry milk, cream, condensed skim milk, and starter and salt. The products produced were Cheddar cheese, fat-reduced whey, cream, whey cream, casein fines, demineralized whey, 34% dried whey protein, 80% dried whey protein, lactose powder, and cow feed. The costs and prices used were based on market data from March 2004 and affected the results. Inputs were separated into components consisting of whey protein, ash, casein, fat, water, and lactose and were then distributed to products through specific constraints and retention equations. A unique 2-step optimization procedure was developed to ensure that the final composition of fat-reduced whey was correct. The model was evaluated for milk compositions ranging from 1.62 to 3.59% casein, 0.41 to 1.14% whey protein, 1.89 to 5.97% fat, and 4.06 to 5.64% lactose. The kappa casein genotype was represented by different retentions of milk components in Cheddar cheese and ranged from 0.715 to 0.7411 kg of casein in cheese/kg of casein in milk and from 0.7795 to 0.9210 kg of fat in cheese/kg of fat in milk. Milk composition had a greater effect on Cheddar cheese production and profit than did genotype. Cheese production was significantly different and ranged from 9,846 kg with a high-casein milk composition to 6,834 kg with a high-fat milk composition per 100,000 kg of milk. Profit (per 100,000 kg of milk) was significantly different, ranging from $70,586 for a high-fat milk composition to $16,490 for a low-fat milk composition. However, cheese production was not significantly different, and profit was significant only for the lowest profit ($40,602) with the kappa-casein genotype. Results from this model analysis showed that the optimization model is useful for determining costs and prices for cheese plant inputs and products, and that it can be used to evaluate the economic value of milk components to optimize cheese plant profits.